system

Abstract

The system according to this embodiment aims to automatically analyze business workflows, propose improvements, and share them. [Solution] The system according to the embodiment comprises an analysis unit, a proposal unit, a sharing unit, and a storage unit. The analysis unit automatically analyzes the business flow. The proposal unit makes improvement suggestions based on the business flow analyzed by the analysis unit. The sharing unit shares the improvement suggestions made by the proposal unit within the group companies. The storage unit stores the improvement suggestions made by the proposal unit and continuously learns from them.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of the chatbot's 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, the efficiency of business processes and the sharing of improvement proposals have not been sufficiently carried out, and there is room for improvement.

[0005] The system according to the embodiment aims to automatically analyze a business process, make improvement proposals, and share them.

Means for Solving the Problems

[0006] The system according to the embodiment includes an analysis unit, a proposal unit, a sharing unit, and a storage unit. The analysis unit automatically analyzes a business process. The proposal unit makes improvement proposals based on the business process analyzed by the analysis unit. The sharing unit shares the improvement proposals made by the proposal unit within group companies. The storage unit stores the improvement proposals made by the proposal unit and continuously learns. [Effects of the Invention]

[0007] The system according to this embodiment can automatically analyze business workflows, propose improvements, and share them. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

[0019] The smart device 14 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. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The business process automatic sharing, storage, and improvement suggestion system according to an embodiment of the present invention is a system that automatically analyzes the business flow of each group company and realizes efficiency improvements, cost reductions, and time savings for business processes that could not be optimized internally. The business process automatic sharing, storage, and improvement suggestion system automatically analyzes the business flow of each group company and realizes efficiency improvements, cost reductions, and time savings for business processes that could not be optimized internally. For example, the business process automatic sharing, storage, and improvement suggestion system automatically analyzes the business flow of each group company. The AI ​​analyzes each company's business process in detail and identifies areas where optimization can be performed. For example, for tasks that involve a lot of routine work, such as data entry and report creation, the AI ​​can suggest ways to improve efficiency. Next, the business process automatic sharing, storage, and improvement suggestion system makes suggestions for improving business processes based on the analysis results. For example, it may suggest automating parts that are currently done manually in a specific business flow, or suggesting a restructuring of the business flow. This improves business efficiency and realizes cost reductions and time savings. Furthermore, the business process automatic sharing, storage, and improvement suggestion system shares the business process improvement suggestions within the group company. Each company can further optimize its business processes by referencing the success stories and know-how of other companies. For example, if an automation tool implemented by one company proves effective, that information can be shared with other companies, allowing them to implement similar tools. Furthermore, the automated business process sharing, storage, and improvement suggestion system stores and continuously learns from suggestions for improving business processes. This allows the system to always provide optimal suggestions based on the latest information, ensuring that business process optimization continues to evolve. For example, when new technologies or tools emerge, the system incorporates them and provides improvement suggestions. Through this mechanism, each group company can achieve business process efficiency, cost reduction, and time savings, thereby improving overall competitiveness. For instance, increased efficiency allows employees to focus on higher-value tasks, improving overall company productivity. Cost reduction also allows companies to invest resources in other important areas.Furthermore, the time savings improve the speed of operations and enable rapid decision-making. As a result, the automated business process sharing, storage, and improvement suggestion system can achieve increased efficiency, cost reduction, and time savings in business processes.

[0029] The automated business process sharing, storage, and improvement suggestion system according to this embodiment comprises an analysis unit, a suggestion unit, a sharing unit, and a storage unit. The analysis unit automatically analyzes business flows. For example, the analysis unit automatically collects business flows from each group company and analyzes them using AI. For example, the analysis unit identifies tasks that involve a lot of routine work, such as data entry and report creation, and extracts areas where efficiency can be improved. The suggestion unit makes improvement suggestions based on the business flows analyzed by the analysis unit. For example, the suggestion unit makes suggestions to automate parts of a specific business flow that are currently done manually. The suggestion unit can also make suggestions to restructure business flows. The sharing unit shares improvement suggestions made by the suggestion unit within the group companies. For example, the sharing unit shares success stories and know-how from other companies. For example, the sharing unit shares information on effective automation tools with other companies and promotes the adoption of similar tools. The storage unit stores improvement suggestions made by the suggestion unit and continuously learns from them. For example, the storage unit incorporates new technologies and tools to make improvement suggestions. The storage unit, for example, stores suggestions for improving business processes in a database and continuously learns using AI. As a result, the automated business process sharing, storage, and improvement suggestion system according to this embodiment achieves business process efficiency, cost reduction, and time savings through automated analysis of business flows, improvement suggestions, sharing, and storage.

[0030] The analysis department automatically analyzes business processes. Specifically, it automatically collects business processes from each group company and analyzes them using AI. For example, it identifies tasks that involve a lot of routine work, such as data entry and report creation, and extracts areas where efficiency improvements can be made. The AI ​​uses natural language processing and machine learning algorithms to analyze each step of the business process in detail. For example, it uses natural language processing to analyze the content of documents and reports included in the business process to identify frequently occurring tasks and manual parts. It also uses machine learning algorithms to learn patterns based on past business data and predict areas where efficiency improvements can be made. Furthermore, the analysis department can collect business process data in real time and perform continuous analysis. This allows for a rapid response to changes in business processes and new challenges. For example, when a new project is started, it can immediately analyze its business process and propose improvements to make it more efficient. The analysis department can also visualize the results of the business process analysis and display them clearly using graphs and charts. This allows business personnel and managers to grasp the current state of the business process and areas for improvement at a glance.

[0031] The proposal department makes improvement suggestions based on the business flow analyzed by the analysis department. Specifically, it proposes automating manual processes within specific business flows. For example, it can propose introducing OCR (optical character recognition) technology to automate data entry. It can also propose introducing an automated generation tool using templates to automate the report creation process. Furthermore, the proposal department can propose restructuring business flows. For example, it can identify bottlenecks in business flows and propose specific methods to improve them. This includes reviewing the division of labor and introducing new tools and technologies. The proposal department can use AI to analyze the effectiveness of past improvement suggestions and select the most effective suggestions. This allows the proposal department to always make optimal improvement suggestions based on the latest information and technology. Furthermore, the proposal department can monitor the effects of the improvement suggestions after implementation and make additional suggestions as needed. This allows the proposal department to support continuous improvement of business processes and achieve increased efficiency and quality.

[0032] The Sharing Department shares improvement proposals made by the Proposal Department within the group companies. Specifically, it shares success stories and know-how from other companies. For example, if an automation tool implemented by a particular company proves effective, the department shares the implementation methods and operational know-how of that tool with other companies. The Sharing Department also shares information on effective automation tools with other companies and promotes the adoption of similar tools. This leads to improved operational efficiency across the entire group. The Sharing Department provides a platform for information sharing, making it easily accessible to each company. For example, it sets up an online knowledge base or forum where improvement proposals and success stories can be posted and viewed. It can also hold regular webinars and workshops to provide a forum for direct information exchange and discussion. Furthermore, the Sharing Department monitors the effectiveness of the shared information and collects feedback. This allows it to evaluate how effective the shared information actually is and update or add information as needed. Through information sharing, the Sharing Department can promote improvements in business processes across the entire group, achieving overall efficiency and enhanced competitiveness.

[0033] The data storage unit accumulates improvement suggestions made by the proposal unit and continuously learns from them. Specifically, it incorporates new technologies and tools into its improvement suggestions. For example, it incorporates the latest AI technologies and automation tools and stores improvement suggestions using them in its database. The data storage unit stores improvement suggestions for business processes in its database and continuously learns from them using AI. This allows it to make more effective improvement suggestions based on past suggestions and their effects. The data storage unit can analyze the information stored in the database to grasp trends and patterns in business processes. For example, it can identify problems and bottlenecks that frequently occur in a particular business flow and make improvement suggestions to address them. The data storage unit can also incorporate best practices from other companies and industries and make improvement suggestions based on them. This allows the data storage unit to always make optimal improvement suggestions based on the latest information and technologies. Furthermore, the data storage unit monitors the effects after the implementation of improvement suggestions and stores the feedback in its database. This allows the data storage unit to achieve increased efficiency and improved quality in business processes through continuous learning and improvement.

[0034] The proposal department can propose automating manual processes within specific workflows. For example, the proposal department can propose automating data entry tasks. The proposal department can also propose automating report creation tasks. The proposal department can also propose automating manual inventory management tasks. This will improve operational efficiency through the automation of manual processes. Some or all of the above processes performed by the proposal department may be carried out using AI, for example, or without AI. For example, the proposal department can propose tools or software to automate manual processes.

[0035] The sharing department can share success stories and know-how from other companies. For example, if an automation tool implemented by one company proves effective, the sharing department can share that information with other companies. The sharing department can also store success stories in a database and make them accessible to other companies. The sharing department can also hold workshops and seminars to share know-how. This promotes the optimization of business processes by sharing success stories and know-how from other companies. Some or all of the above processes in the sharing department may be performed using AI, for example, or not using AI. For example, the sharing department could use an AI system that automatically collects success stories and know-how and notifies other companies.

[0036] The data storage unit can incorporate new technologies and tools to make improvement suggestions. For example, if a new automation tool becomes available, the data storage unit can incorporate it and make improvement suggestions. The data storage unit can also incorporate new data analysis technologies to make improvement suggestions for business processes. The data storage unit can also incorporate new project management tools to make improvements to business operations. In this way, by incorporating new technologies and tools, the latest improvement suggestions are always provided. Some or all of the above processes in the data storage unit may be performed using AI, for example, or not using AI. For example, the data storage unit can use an AI system that automatically collects information on new technologies and tools and reflects it in improvement suggestions.

[0037] The analysis department can analyze tasks that involve a lot of routine work, such as data entry and report creation. For example, the analysis department can analyze regular data entry tasks and identify areas for improvement. For example, the analysis department can analyze routine report creation tasks and propose automation. For example, the analysis department can identify time-consuming parts of routine tasks and propose improvements. In this way, the analysis of routine tasks leads to increased efficiency. Some or all of the above-mentioned processes in the analysis department may be performed using AI, for example, or not. For example, the analysis department can collect data on routine tasks and analyze it using AI to propose improvements for efficiency.

[0038] The proposal department can propose restructuring business processes. For example, the proposal department can review business processes and propose efficient flows. The proposal department can also propose the introduction of tools or software to optimize business processes. The proposal department can also propose training programs to restructure business processes. This will optimize business processes by restructuring them. Some or all of the above processes performed by the proposal department may be carried out using AI, for example, or not. For example, the proposal department can collect business process data and use AI to propose the optimal flow.

[0039] The analysis department can optimize its analysis algorithm by referring to past business data when analyzing business flows. For example, the analysis department can identify frequently occurring problems based on past business data and adjust the analysis algorithm. The analysis department can also extract successful business flows from past business data and reflect them in the analysis algorithm. For example, the analysis department can analyze past business data and construct an analysis algorithm that takes into account seasonal fluctuations in business. This improves the accuracy of the analysis algorithm by referring to past business data. Some or all of the above processes in the analysis department may be performed using AI, for example, or without AI. For example, the analysis department can collect past business data and optimize the analysis algorithm using AI.

[0040] The analysis department can apply different analysis methods to each type of task when analyzing business workflows. For example, the analysis department might apply an analysis method that emphasizes input speed and accuracy to data entry tasks. For example, the analysis department might apply an analysis method that evaluates the structure and content quality of reports to report creation tasks. For example, the analysis department might apply an analysis method that evaluates customer satisfaction and response time to customer service tasks. This improves the accuracy of the analysis by applying analysis methods appropriate to the type of task. Some or all of the above-described processes in the analysis department may be performed using AI, for example, or not. For example, the analysis department can collect different data for each type of task and apply the optimal analysis method using AI.

[0041] The analysis department can perform business process analysis while considering the geographical distribution of business processes. For example, the analysis department can individually analyze business processes in each region and make optimization suggestions for each region. For example, the analysis department can identify different business process patterns based on geographical distribution and reflect them in the analysis. For example, the analysis department can perform business process analysis while considering geographical factors (traffic, climate, etc.). This makes it possible to make optimization suggestions for each region through analysis that takes geographical distribution into account. Some or all of the above processes in the analysis department may be performed using AI, for example, or not using AI. For example, the analysis department can collect business process data using a geographic information system (GIS) and perform analysis using AI.

[0042] The analysis department can improve the accuracy of its analysis by referring to relevant business literature when analyzing business flows. For example, the analysis department can refer to the latest research papers related to business flows and improve its analysis algorithms. The analysis department can also refer to best practices related to business flows and reflect them in the analysis results. The analysis department can also refer to industry standards related to business flows and improve the accuracy of its analysis. Thus, the accuracy of the analysis is improved by referring to relevant business literature. Some or all of the above processes in the analysis department may be performed using AI, for example, or not using AI. For example, the analysis department can collect a database of business literature and optimize its analysis algorithms using AI.

[0043] The proposal department can adjust the level of detail in improvement proposals based on the importance of the task. For example, the proposal department can provide detailed improvement proposals for high-importance tasks, and concise proposals for low-importance tasks. The proposal department can also prioritize proposals according to the importance of the task. This allows for more effective improvement proposals by adjusting the level of detail according to the importance of the task. Some or all of the above processes in the proposal department may be performed using AI, for example, or not. For example, the proposal department can evaluate the importance of a task and adjust the level of detail of the proposal using AI.

[0044] The proposal department can apply different proposal algorithms depending on the category of work when making improvement suggestions. For example, the proposal department might suggest the introduction of automation tools for data entry tasks. For example, it might suggest the use of templates for report creation tasks. For example, it might suggest the introduction of a chatbot for customer service tasks. This allows for more appropriate improvement suggestions to be provided by applying proposal algorithms tailored to the category of work. Some or all of the above-described processes in the proposal department may be performed using AI, for example, or not. For example, the proposal department can collect different data for each category of work and apply the optimal proposal algorithm using AI.

[0045] The proposal department can prioritize improvement proposals based on the submission deadline for each task. For example, the proposal department will prioritize proposals that can be implemented quickly for tasks with approaching deadlines. For example, the proposal department may also provide detailed improvement proposals for tasks with later submission deadlines. The proposal department can also set priority levels for proposals according to their submission dates. This allows for more effective improvement proposals by prioritizing proposals based on their submission dates. Some or all of the above processes in the proposal department may be performed using AI, for example, or not. For example, the proposal department can evaluate the submission dates of tasks and use AI to determine the priority level of proposals.

[0046] The proposal department can adjust the order of improvement proposals based on the relevance of the tasks. For example, the proposal department can make proposals consecutively for highly relevant tasks. For example, the proposal department can also make proposals individually for less relevant tasks. The proposal department can also set the order of proposals according to the relevance of the tasks. This allows for more effective improvement proposals to be provided by adjusting the order of proposals according to the relevance of the tasks. Some or all of the above processing in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can evaluate the relevance of tasks and adjust the order of proposals using AI.

[0047] The sharing unit can select the optimal display method when displaying shared information by referring to the user's past operation history. For example, the sharing unit can prioritize providing display methods that the user has previously preferred. For example, the sharing unit can also suggest the optimal display method based on the user's past operation history. For example, the sharing unit can analyze the user's past operation history and provide a display method with high visibility. In this way, the optimal display method is provided by referring to the user's past operation history. Some or all of the above processing in the sharing unit may be performed using AI, for example, or without AI. For example, the sharing unit can collect user operation history data and select the optimal display method using AI.

[0048] The sharing unit can select the optimal display method when displaying shared information, taking into account the user's device information. For example, if the user is using a smartphone, the sharing unit can provide a display method that matches the screen size. For example, if the user is using a tablet, the sharing unit can also provide a display method optimized for a larger screen. For example, if the user is using a smartwatch, the sharing unit can also provide a concise and highly visible display method. In this way, the optimal display method is provided by taking into account the user's device information. Some or all of the above processing in the sharing unit may be performed using AI, for example, or without AI. For example, the sharing unit can collect the user's device information and select the optimal display method using AI.

[0049] The storage unit can optimize its storage algorithm by referring to past stored data during storage. For example, the storage unit prioritizes storing frequently used data based on past stored data. The storage unit can also perform efficient storage by, for example, excluding unnecessary data from past stored data. The storage unit can also analyze past stored data and construct an optimal storage algorithm. This improves the accuracy of the storage algorithm by referring to past stored data. Some or all of the above processes in the storage unit may be performed using AI, for example, or without AI. For example, the storage unit can collect past stored data and optimize its storage algorithm using AI.

[0050] The storage unit can weight the stored data based on the submission timing of the business process flows during storage. For example, the storage unit can prioritize storing and weighting data from business processes with upcoming submission times. For example, the storage unit can also store data from business processes with later submission times with lighter weighting. The storage unit can also dynamically adjust the data weighting according to the submission timing. This ensures that more important data is prioritized for storage based on the data weighting according to the submission timing of the business processes. Some or all of the above processing in the storage unit may be performed using AI, for example, or without AI. For example, the storage unit can evaluate the submission timing of business processes and weight the data using AI.

[0051] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0052] The automated business process sharing, storage, and improvement suggestion system can further optimize its analysis algorithms by referencing past business data during business flow analysis. For example, the analysis department can identify frequently occurring problems based on past business data and adjust the analysis algorithm accordingly. It can also extract successful business flows from past business data and incorporate them into the analysis algorithm. Furthermore, it can analyze past business data to construct an analysis algorithm that takes into account seasonal fluctuations in business operations. This improves the accuracy of the analysis algorithm by referencing past business data. Some or all of the above processes in the analysis department may be performed using AI or not. For example, the analysis department can collect past business data and optimize the analysis algorithm using AI.

[0053] The automated business process sharing, storage, and improvement suggestion system can further apply different analysis methods to each type of business process during business flow analysis. For example, the analysis department can apply an analysis method that emphasizes input speed and accuracy to data entry tasks. For report creation tasks, it can apply an analysis method that evaluates the structure and content quality of the document. For customer service tasks, it can apply an analysis method that evaluates customer satisfaction and response time. This improves the accuracy of the analysis by applying an analysis method appropriate to the type of business process. Some or all of the above processing in the analysis department may be performed using AI or not. For example, the analysis department can collect different data for each type of business and apply the optimal analysis method using AI.

[0054] The automated business process sharing, storage, and improvement suggestion system can also perform business flow analysis while considering the geographical distribution of business processes. For example, the analysis department can individually analyze business flows in each region and make optimization suggestions for each region. Based on geographical distribution, it can also identify different business flow patterns and reflect them in the analysis. Furthermore, it can analyze business flows while considering geographical factors (traffic, climate, etc.). This makes it possible to make optimization suggestions for each region through analysis that takes geographical distribution into account. Some or all of the above processing in the analysis department may be performed using AI or not. For example, the analysis department can collect business flow data using a geographic information system (GIS) and perform analysis using AI.

[0055] The automated business process sharing, storage, and improvement suggestion system can further improve the accuracy of its analysis by referring to relevant business literature during business flow analysis. For example, the analysis department can refer to the latest research papers related to business flows and improve its analysis algorithms. It can also refer to best practices related to business flows and reflect them in the analysis results. Furthermore, it can refer to industry standards related to business flows to improve the accuracy of the analysis. In this way, the accuracy of the analysis is improved by referring to relevant business literature. Some or all of the above processes in the analysis department may be performed using AI or not. For example, the analysis department can collect a database of business literature and optimize its analysis algorithms using AI.

[0056] The automated business process sharing, storage, and improvement suggestion system can further allow the suggestion department to adjust the level of detail of suggestions based on the importance of the task. For example, the suggestion department can provide detailed improvement suggestions for high-importance tasks and concise suggestions for low-importance tasks. It can also prioritize suggestions according to the importance of the task. This allows for more effective improvement suggestions to be provided by adjusting the level of detail according to the importance of the task. Some or all of the above processes in the suggestion department may be performed using AI or not. For example, the suggestion department can evaluate the importance of a task and adjust the level of detail of the suggestion using AI.

[0057] The following briefly describes the processing flow for example form 1.

[0058] Step 1: The analysis department automatically analyzes business processes. Specifically, it automatically collects business processes from each group company and analyzes them using AI. For example, it identifies tasks that involve a lot of routine work, such as data entry and report creation, and extracts areas where there is room for improvement. Step 2: The proposal department makes improvement suggestions based on the business flow analyzed by the analysis department. Specifically, they propose automating manual parts of a particular business flow or restructuring the business flow. Step 3: The Sharing Department shares the improvement proposals made by the Proposal Department within the group companies. Specifically, they share success stories and know-how from other companies and information on effective automation tools to promote the adoption of similar tools. Step 4: The storage unit stores improvement suggestions made by the proposal unit and continuously learns from them. Specifically, it incorporates new technologies and tools to make improvement suggestions, stores these business process improvement suggestions in a database, and continuously learns from them using AI.

[0059] (Example of form 2) The business process automatic sharing, storage, and improvement suggestion system according to an embodiment of the present invention is a system that automatically analyzes the business flow of each group company and realizes efficiency improvements, cost reductions, and time savings for business processes that could not be optimized internally. The business process automatic sharing, storage, and improvement suggestion system automatically analyzes the business flow of each group company and realizes efficiency improvements, cost reductions, and time savings for business processes that could not be optimized internally. For example, the business process automatic sharing, storage, and improvement suggestion system automatically analyzes the business flow of each group company. The AI ​​analyzes each company's business process in detail and identifies areas where optimization can be performed. For example, for tasks that involve a lot of routine work, such as data entry and report creation, the AI ​​can suggest ways to improve efficiency. Next, the business process automatic sharing, storage, and improvement suggestion system makes suggestions for improving business processes based on the analysis results. For example, it may suggest automating parts that are currently done manually in a specific business flow, or suggesting a restructuring of the business flow. This improves business efficiency and realizes cost reductions and time savings. Furthermore, the business process automatic sharing, storage, and improvement suggestion system shares the business process improvement suggestions within the group company. Each company can further optimize its business processes by referencing the success stories and know-how of other companies. For example, if an automation tool implemented by one company proves effective, that information can be shared with other companies, allowing them to implement similar tools. Furthermore, the automated business process sharing, storage, and improvement suggestion system stores and continuously learns from suggestions for improving business processes. This allows the system to always provide optimal suggestions based on the latest information, ensuring that business process optimization continues to evolve. For example, when new technologies or tools emerge, the system incorporates them and provides improvement suggestions. Through this mechanism, each group company can achieve business process efficiency, cost reduction, and time savings, thereby improving overall competitiveness. For instance, increased efficiency allows employees to focus on higher-value tasks, improving overall company productivity. Cost reduction also allows companies to invest resources in other important areas.Furthermore, the time savings improve the speed of operations and enable rapid decision-making. As a result, the automated business process sharing, storage, and improvement suggestion system can achieve increased efficiency, cost reduction, and time savings in business processes.

[0060] The automated business process sharing, storage, and improvement suggestion system according to this embodiment comprises an analysis unit, a suggestion unit, a sharing unit, and a storage unit. The analysis unit automatically analyzes business flows. For example, the analysis unit automatically collects business flows from each group company and analyzes them using AI. For example, the analysis unit identifies tasks that involve a lot of routine work, such as data entry and report creation, and extracts areas where efficiency can be improved. The suggestion unit makes improvement suggestions based on the business flows analyzed by the analysis unit. For example, the suggestion unit makes suggestions to automate parts of a specific business flow that are currently done manually. The suggestion unit can also make suggestions to restructure business flows. The sharing unit shares improvement suggestions made by the suggestion unit within the group companies. For example, the sharing unit shares success stories and know-how from other companies. For example, the sharing unit shares information on effective automation tools with other companies and promotes the adoption of similar tools. The storage unit stores improvement suggestions made by the suggestion unit and continuously learns from them. For example, the storage unit incorporates new technologies and tools to make improvement suggestions. The storage unit, for example, stores suggestions for improving business processes in a database and continuously learns using AI. As a result, the automated business process sharing, storage, and improvement suggestion system according to this embodiment achieves business process efficiency, cost reduction, and time savings through automated analysis of business flows, improvement suggestions, sharing, and storage.

[0061] The analysis department automatically analyzes business processes. Specifically, it automatically collects business processes from each group company and analyzes them using AI. For example, it identifies tasks that involve a lot of routine work, such as data entry and report creation, and extracts areas where efficiency improvements can be made. The AI ​​uses natural language processing and machine learning algorithms to analyze each step of the business process in detail. For example, it uses natural language processing to analyze the content of documents and reports included in the business process to identify frequently occurring tasks and manual parts. It also uses machine learning algorithms to learn patterns based on past business data and predict areas where efficiency improvements can be made. Furthermore, the analysis department can collect business process data in real time and perform continuous analysis. This allows for a rapid response to changes in business processes and new challenges. For example, when a new project is started, it can immediately analyze its business process and propose improvements to make it more efficient. The analysis department can also visualize the results of the business process analysis and display them clearly using graphs and charts. This allows business personnel and managers to grasp the current state of the business process and areas for improvement at a glance.

[0062] The proposal department makes improvement suggestions based on the business flow analyzed by the analysis department. Specifically, it proposes automating manual processes within specific business flows. For example, it can propose introducing OCR (optical character recognition) technology to automate data entry. It can also propose introducing an automated generation tool using templates to automate the report creation process. Furthermore, the proposal department can propose restructuring business flows. For example, it can identify bottlenecks in business flows and propose specific methods to improve them. This includes reviewing the division of labor and introducing new tools and technologies. The proposal department can use AI to analyze the effectiveness of past improvement suggestions and select the most effective suggestions. This allows the proposal department to always make optimal improvement suggestions based on the latest information and technology. Furthermore, the proposal department can monitor the effects of the improvement suggestions after implementation and make additional suggestions as needed. This allows the proposal department to support continuous improvement of business processes and achieve increased efficiency and quality.

[0063] The Sharing Department shares improvement proposals made by the Proposal Department within the group companies. Specifically, it shares success stories and know-how from other companies. For example, if an automation tool implemented by a particular company proves effective, the department shares the implementation methods and operational know-how of that tool with other companies. The Sharing Department also shares information on effective automation tools with other companies and promotes the adoption of similar tools. This leads to improved operational efficiency across the entire group. The Sharing Department provides a platform for information sharing, making it easily accessible to each company. For example, it sets up an online knowledge base or forum where improvement proposals and success stories can be posted and viewed. It can also hold regular webinars and workshops to provide a forum for direct information exchange and discussion. Furthermore, the Sharing Department monitors the effectiveness of the shared information and collects feedback. This allows it to evaluate how effective the shared information actually is and update or add information as needed. Through information sharing, the Sharing Department can promote improvements in business processes across the entire group, achieving overall efficiency and enhanced competitiveness.

[0064] The data storage unit accumulates improvement suggestions made by the proposal unit and continuously learns from them. Specifically, it incorporates new technologies and tools into its improvement suggestions. For example, it incorporates the latest AI technologies and automation tools and stores improvement suggestions using them in its database. The data storage unit stores improvement suggestions for business processes in its database and continuously learns from them using AI. This allows it to make more effective improvement suggestions based on past suggestions and their effects. The data storage unit can analyze the information stored in the database to grasp trends and patterns in business processes. For example, it can identify problems and bottlenecks that frequently occur in a particular business flow and make improvement suggestions to address them. The data storage unit can also incorporate best practices from other companies and industries and make improvement suggestions based on them. This allows the data storage unit to always make optimal improvement suggestions based on the latest information and technologies. Furthermore, the data storage unit monitors the effects after the implementation of improvement suggestions and stores the feedback in its database. This allows the data storage unit to achieve increased efficiency and improved quality in business processes through continuous learning and improvement.

[0065] The proposal department can propose automating manual processes within specific workflows. For example, the proposal department can propose automating data entry tasks. The proposal department can also propose automating report creation tasks. The proposal department can also propose automating manual inventory management tasks. This will improve operational efficiency through the automation of manual processes. Some or all of the above processes performed by the proposal department may be carried out using AI, for example, or without AI. For example, the proposal department can propose tools or software to automate manual processes.

[0066] The sharing department can share success stories and know-how from other companies. For example, if an automation tool implemented by one company proves effective, the sharing department can share that information with other companies. The sharing department can also store success stories in a database and make them accessible to other companies. The sharing department can also hold workshops and seminars to share know-how. This promotes the optimization of business processes by sharing success stories and know-how from other companies. Some or all of the above processes in the sharing department may be performed using AI, for example, or not using AI. For example, the sharing department could use an AI system that automatically collects success stories and know-how and notifies other companies.

[0067] The data storage unit can incorporate new technologies and tools to make improvement suggestions. For example, if a new automation tool becomes available, the data storage unit can incorporate it and make improvement suggestions. The data storage unit can also incorporate new data analysis technologies to make improvement suggestions for business processes. The data storage unit can also incorporate new project management tools to make improvements to business operations. In this way, by incorporating new technologies and tools, the latest improvement suggestions are always provided. Some or all of the above processes in the data storage unit may be performed using AI, for example, or not using AI. For example, the data storage unit can use an AI system that automatically collects information on new technologies and tools and reflects it in improvement suggestions.

[0068] The analysis department can analyze tasks that involve a lot of routine work, such as data entry and report creation. For example, the analysis department can analyze regular data entry tasks and identify areas for improvement. For example, the analysis department can analyze routine report creation tasks and propose automation. For example, the analysis department can identify time-consuming parts of routine tasks and propose improvements. In this way, the analysis of routine tasks leads to increased efficiency. Some or all of the above-mentioned processes in the analysis department may be performed using AI, for example, or not. For example, the analysis department can collect data on routine tasks and analyze it using AI to propose improvements for efficiency.

[0069] The proposal department can propose restructuring business processes. For example, the proposal department can review business processes and propose efficient flows. The proposal department can also propose the introduction of tools or software to optimize business processes. The proposal department can also propose training programs to restructure business processes. This will optimize business processes by restructuring them. Some or all of the above processes performed by the proposal department may be carried out using AI, for example, or not. For example, the proposal department can collect business process data and use AI to propose the optimal flow.

[0070] The analysis unit can estimate the user's emotions and adjust the analysis method of the workflow based on the estimated user emotions. For example, if the user is stressed, the analysis unit can reduce the level of detail in the analysis and provide concise results. For example, if the user is relaxed, the analysis unit can also provide detailed analysis results and deeper insights. For example, if the user is in a hurry, the analysis unit can perform a rapid analysis and provide results immediately. This allows for more appropriate analysis results by adjusting the analysis method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can collect user emotion data, estimate emotions using generative AI, and adjust the analysis method based on the results.

[0071] The analysis department can optimize its analysis algorithm by referring to past business data when analyzing business flows. For example, the analysis department can identify frequently occurring problems based on past business data and adjust the analysis algorithm. The analysis department can also extract successful business flows from past business data and reflect them in the analysis algorithm. For example, the analysis department can analyze past business data and construct an analysis algorithm that takes into account seasonal fluctuations in business. This improves the accuracy of the analysis algorithm by referring to past business data. Some or all of the above processes in the analysis department may be performed using AI, for example, or without AI. For example, the analysis department can collect past business data and optimize the analysis algorithm using AI.

[0072] The analysis department can apply different analysis methods to each type of task when analyzing business workflows. For example, the analysis department might apply an analysis method that emphasizes input speed and accuracy to data entry tasks. For example, the analysis department might apply an analysis method that evaluates the structure and content quality of reports to report creation tasks. For example, the analysis department might apply an analysis method that evaluates customer satisfaction and response time to customer service tasks. This improves the accuracy of the analysis by applying analysis methods appropriate to the type of task. Some or all of the above-described processes in the analysis department may be performed using AI, for example, or not. For example, the analysis department can collect different data for each type of task and apply the optimal analysis method using AI.

[0073] 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 can provide a simple and highly visible display method. For example, if the user is relaxed, the analysis unit can also provide a display method that includes detailed information. For example, if the user is in a hurry, the analysis unit can also provide a display method that gets straight to the point. This allows for more appropriate analysis results to be provided by adjusting the display method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can collect user emotion data, estimate emotions using generative AI, and adjust the display method based on the results.

[0074] The analysis department can perform business process analysis while considering the geographical distribution of business processes. For example, the analysis department can individually analyze business processes in each region and make optimization suggestions for each region. For example, the analysis department can identify different business process patterns based on geographical distribution and reflect them in the analysis. For example, the analysis department can perform business process analysis while considering geographical factors (traffic, climate, etc.). This makes it possible to make optimization suggestions for each region through analysis that takes geographical distribution into account. Some or all of the above processes in the analysis department may be performed using AI, for example, or not using AI. For example, the analysis department can collect business process data using a geographic information system (GIS) and perform analysis using AI.

[0075] The analysis department can improve the accuracy of its analysis by referring to relevant business literature when analyzing business flows. For example, the analysis department can refer to the latest research papers related to business flows and improve its analysis algorithms. The analysis department can also refer to best practices related to business flows and reflect them in the analysis results. The analysis department can also refer to industry standards related to business flows and improve the accuracy of its analysis. Thus, the accuracy of the analysis is improved by referring to relevant business literature. Some or all of the above processes in the analysis department may be performed using AI, for example, or not using AI. For example, the analysis department can collect a database of business literature and optimize its analysis algorithms using AI.

[0076] The suggestion unit can estimate the user's emotions and adjust the way improvement suggestions are presented based on the estimated emotions. For example, if the user is stressed, the suggestion unit will provide concise and easy-to-understand suggestions. If the user is relaxed, the suggestion unit may also provide suggestions that include detailed explanations. If the user is in a hurry, the suggestion unit may also provide suggestions that can be implemented quickly. This allows for more appropriate improvement suggestions to be provided by adjusting the presentation according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can collect user emotion data, estimate emotions using generative AI, and adjust the presentation of suggestions based on the results.

[0077] The proposal department can adjust the level of detail in improvement proposals based on the importance of the task. For example, the proposal department can provide detailed improvement proposals for high-importance tasks, and concise proposals for low-importance tasks. The proposal department can also prioritize proposals according to the importance of the task. This allows for more effective improvement proposals by adjusting the level of detail according to the importance of the task. Some or all of the above processes in the proposal department may be performed using AI, for example, or not. For example, the proposal department can evaluate the importance of a task and adjust the level of detail of the proposal using AI.

[0078] The proposal department can apply different proposal algorithms depending on the category of work when making improvement suggestions. For example, the proposal department might suggest the introduction of automation tools for data entry tasks. For example, it might suggest the use of templates for report creation tasks. For example, it might suggest the introduction of a chatbot for customer service tasks. This allows for more appropriate improvement suggestions to be provided by applying proposal algorithms tailored to the category of work. Some or all of the above-described processes in the proposal department may be performed using AI, for example, or not. For example, the proposal department can collect different data for each category of work and apply the optimal proposal algorithm using AI.

[0079] The suggestion unit can estimate the user's emotions and prioritize suggestions based on those emotions. For example, if the user is stressed, the suggestion unit will prioritize the most effective suggestion. If the user is relaxed, the suggestion unit may offer multiple suggestions to increase the options. If the user is in a hurry, the suggestion unit may prioritize suggestions that can be implemented quickly. This allows for more effective improvement suggestions to be provided by prioritizing suggestions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the suggestion unit may be performed using AI or not. For example, the suggestion unit can collect user emotion data, estimate emotions using generative AI, and prioritize suggestions based on the results.

[0080] The proposal department can prioritize improvement proposals based on the submission deadline for each task. For example, the proposal department will prioritize proposals that can be implemented quickly for tasks with approaching deadlines. For example, the proposal department may also provide detailed improvement proposals for tasks with later submission deadlines. The proposal department can also set priority levels for proposals according to their submission dates. This allows for more effective improvement proposals by prioritizing proposals based on their submission dates. Some or all of the above processes in the proposal department may be performed using AI, for example, or not. For example, the proposal department can evaluate the submission dates of tasks and use AI to determine the priority level of proposals.

[0081] The proposal department can adjust the order of improvement proposals based on the relevance of the tasks. For example, the proposal department can make proposals consecutively for highly relevant tasks. For example, the proposal department can also make proposals individually for less relevant tasks. The proposal department can also set the order of proposals according to the relevance of the tasks. This allows for more effective improvement proposals to be provided by adjusting the order of proposals according to the relevance of the tasks. Some or all of the above processing in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can evaluate the relevance of tasks and adjust the order of proposals using AI.

[0082] The sharing section can estimate the user's emotions and adjust how shared information is displayed based on the estimated emotions. For example, if the user is nervous, the sharing section can provide a simple and highly visible display method. For example, if the user is relaxed, the sharing section can also provide a display method that includes detailed information. For example, if the user is in a hurry, the sharing section can also provide a display method that gets straight to the point. This allows for the provision of more appropriate shared information by adjusting the display method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the sharing section may be performed using AI, for example, or without AI. For example, the sharing section can collect user emotion data, estimate emotions using generative AI, and adjust the display method based on the results.

[0083] The sharing unit can select the optimal display method when displaying shared information by referring to the user's past operation history. For example, the sharing unit can prioritize providing display methods that the user has previously preferred. For example, the sharing unit can also suggest the optimal display method based on the user's past operation history. For example, the sharing unit can analyze the user's past operation history and provide a display method with high visibility. In this way, the optimal display method is provided by referring to the user's past operation history. Some or all of the above processing in the sharing unit may be performed using AI, for example, or without AI. For example, the sharing unit can collect user operation history data and select the optimal display method using AI.

[0084] The sharing section can estimate the user's emotions and prioritize shared information based on the estimated emotions. For example, if the user is stressed, the sharing section will prioritize displaying the most important information. For example, if the user is relaxed, the sharing section may provide a display method that includes detailed information. For example, if the user is in a hurry, the sharing section may prioritize displaying concise information. This ensures that more important information is provided preferentially by prioritizing information according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the sharing section may be performed using AI or not. For example, the sharing section can collect user emotion data, estimate emotions using generative AI, and determine priorities based on the results.

[0085] The sharing unit can select the optimal display method when displaying shared information, taking into account the user's device information. For example, if the user is using a smartphone, the sharing unit can provide a display method that matches the screen size. For example, if the user is using a tablet, the sharing unit can also provide a display method optimized for a larger screen. For example, if the user is using a smartwatch, the sharing unit can also provide a concise and highly visible display method. In this way, the optimal display method is provided by taking into account the user's device information. Some or all of the above processing in the sharing unit may be performed using AI, for example, or without AI. For example, the sharing unit can collect the user's device information and select the optimal display method using AI.

[0086] The data storage unit can estimate the user's emotions and select data to store based on the estimated emotions. For example, if the user is stressed, the storage unit will store only important data. If the user is relaxed, the storage unit can also store detailed data. If the user is in a hurry, the storage unit can prioritize data that can be stored quickly. This ensures that more important data is stored by selecting data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the storage unit may be performed using AI or not. For example, the storage unit can collect user emotion data, estimate emotions using generative AI, and select data to store based on the results.

[0087] The storage unit can optimize its storage algorithm by referring to past stored data during storage. For example, the storage unit prioritizes storing frequently used data based on past stored data. The storage unit can also perform efficient storage by, for example, excluding unnecessary data from past stored data. The storage unit can also analyze past stored data and construct an optimal storage algorithm. This improves the accuracy of the storage algorithm by referring to past stored data. Some or all of the above processes in the storage unit may be performed using AI, for example, or without AI. For example, the storage unit can collect past stored data and optimize its storage algorithm using AI.

[0088] The data storage unit can estimate the user's emotions and adjust the storage frequency based on the estimated emotions. For example, if the user is stressed, the storage unit can reduce the storage frequency and store only important data. For example, if the user is relaxed, the storage unit can store detailed data more frequently. For example, if the user is in a hurry, the storage unit can prioritize data that can be stored quickly. This allows for the storage of more appropriate data by adjusting the storage frequency according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the storage unit may be performed using AI or not. For example, the storage unit can collect user emotion data, estimate emotions using generative AI, and adjust the storage frequency based on the results.

[0089] The storage unit can weight the stored data based on the submission timing of the business process flows during storage. For example, the storage unit can prioritize storing and weighting data from business processes with upcoming submission times. For example, the storage unit can also store data from business processes with later submission times with lighter weighting. The storage unit can also dynamically adjust the data weighting according to the submission timing. This ensures that more important data is prioritized for storage based on the data weighting according to the submission timing of the business processes. Some or all of the above processing in the storage unit may be performed using AI, for example, or without AI. For example, the storage unit can evaluate the submission timing of business processes and weight the data using AI.

[0090] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0091] The automated business process sharing, storage, and improvement suggestion system can further estimate user emotions and make improvement suggestions for business processes based on those estimated emotions. For example, if a user is feeling stressed, the suggestion unit can make concise and easy-to-implement improvement suggestions. If the user is relaxed, it can also make suggestions that include detailed explanations. If the user is in a hurry, it can prioritize improvement suggestions that can be implemented quickly. This provides improvement suggestions tailored to the user's emotions, leading to more effective optimization of business processes. Emotion estimation is achieved using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can collect user emotion data, estimate emotions using generative AI, and make improvement suggestions based on the results.

[0092] The automated business process sharing, storage, and improvement suggestion system can further estimate the user's emotions and adjust the way the business flow analysis results are displayed based on the estimated emotions. For example, the analysis unit can provide a simple and highly visible display method when the user is stressed. When the user is relaxed, it can provide a display method that includes detailed information. When the user is in a hurry, it can provide a display method that gets straight to the point. This allows for more appropriate analysis results to be provided by adjusting the display method according to the user's emotions. Emotion estimation is achieved using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can collect user emotion data, estimate emotions using generative AI, and adjust the display method based on the results.

[0093] The automated business process sharing, storage, and improvement suggestion system can further estimate the user's emotions and adjust the display method of shared information based on the estimated emotions. For example, the sharing section can provide a simple and highly visible display method when the user is stressed. When the user is relaxed, it can provide a display method that includes detailed information. Also, when the user is in a hurry, it can provide a display method that gets straight to the point. This allows for the provision of more appropriate shared information by adjusting the display method according to the user's emotions. Emotion estimation is achieved using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the sharing section may be performed using AI or not. For example, the sharing section can collect user emotion data, estimate emotions using generative AI, and adjust the display method based on the results.

[0094] The automated business process sharing, storage, and improvement suggestion system can further estimate the user's emotions and select storage data based on the estimated emotions. For example, if the user is feeling stressed, the storage unit can store only important data. If the user is relaxed, it can store detailed data. If the user is in a hurry, it can prioritize data that can be stored quickly. This ensures that more important data is stored by selecting data according to the user's emotions. Emotion estimation is achieved using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the storage unit may be performed using AI or not. For example, the storage unit can collect user emotion data, estimate emotions using generative AI, and select storage data based on the results.

[0095] The automated business process sharing, storage, and improvement suggestion system can further estimate the user's emotions and prioritize suggestions based on those emotions. For example, if the user is stressed, the suggestion system can prioritize the most effective suggestion. If the user is relaxed, it can offer multiple suggestions to increase their options. If the user is in a hurry, it can prioritize suggestions that can be implemented quickly. This allows for more effective improvement suggestions to be provided by prioritizing suggestions according to the user's emotions. Emotion estimation is achieved using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion system may be performed using AI or not. For example, the suggestion system can collect user emotion data, estimate emotions using generative AI, and prioritize suggestions based on the results.

[0096] The automated business process sharing, storage, and improvement suggestion system can further optimize its analysis algorithms by referencing past business data during business flow analysis. For example, the analysis department can identify frequently occurring problems based on past business data and adjust the analysis algorithm accordingly. It can also extract successful business flows from past business data and incorporate them into the analysis algorithm. Furthermore, it can analyze past business data to construct an analysis algorithm that takes into account seasonal fluctuations in business operations. This improves the accuracy of the analysis algorithm by referencing past business data. Some or all of the above processes in the analysis department may be performed using AI or not. For example, the analysis department can collect past business data and optimize the analysis algorithm using AI.

[0097] The automated business process sharing, storage, and improvement suggestion system can further apply different analysis methods to each type of business process during business flow analysis. For example, the analysis department can apply an analysis method that emphasizes input speed and accuracy to data entry tasks. For report creation tasks, it can apply an analysis method that evaluates the structure and content quality of the document. For customer service tasks, it can apply an analysis method that evaluates customer satisfaction and response time. This improves the accuracy of the analysis by applying an analysis method appropriate to the type of business process. Some or all of the above processing in the analysis department may be performed using AI or not. For example, the analysis department can collect different data for each type of business and apply the optimal analysis method using AI.

[0098] The automated business process sharing, storage, and improvement suggestion system can also perform business flow analysis while considering the geographical distribution of business processes. For example, the analysis department can individually analyze business flows in each region and make optimization suggestions for each region. Based on geographical distribution, it can also identify different business flow patterns and reflect them in the analysis. Furthermore, it can analyze business flows while considering geographical factors (traffic, climate, etc.). This makes it possible to make optimization suggestions for each region through analysis that takes geographical distribution into account. Some or all of the above processing in the analysis department may be performed using AI or not. For example, the analysis department can collect business flow data using a geographic information system (GIS) and perform analysis using AI.

[0099] The automated business process sharing, storage, and improvement suggestion system can further improve the accuracy of its analysis by referring to relevant business literature during business flow analysis. For example, the analysis department can refer to the latest research papers related to business flows and improve its analysis algorithms. It can also refer to best practices related to business flows and reflect them in the analysis results. Furthermore, it can refer to industry standards related to business flows to improve the accuracy of the analysis. In this way, the accuracy of the analysis is improved by referring to relevant business literature. Some or all of the above processes in the analysis department may be performed using AI or not. For example, the analysis department can collect a database of business literature and optimize its analysis algorithms using AI.

[0100] The automated business process sharing, storage, and improvement suggestion system can further allow the suggestion department to adjust the level of detail of suggestions based on the importance of the task. For example, the suggestion department can provide detailed improvement suggestions for high-importance tasks and concise suggestions for low-importance tasks. It can also prioritize suggestions according to the importance of the task. This allows for more effective improvement suggestions to be provided by adjusting the level of detail according to the importance of the task. Some or all of the above processes in the suggestion department may be performed using AI or not. For example, the suggestion department can evaluate the importance of a task and adjust the level of detail of the suggestion using AI.

[0101] The following briefly describes the processing flow for example form 2.

[0102] Step 1: The analysis department automatically analyzes business processes. Specifically, it automatically collects business processes from each group company and analyzes them using AI. For example, it identifies tasks that involve a lot of routine work, such as data entry and report creation, and extracts areas where there is room for improvement. Step 2: The proposal department makes improvement suggestions based on the business flow analyzed by the analysis department. Specifically, they propose automating manual parts of a particular business flow or restructuring the business flow. Step 3: The Sharing Department shares the improvement proposals made by the Proposal Department within the group companies. Specifically, they share success stories and know-how from other companies and information on effective automation tools to promote the adoption of similar tools. Step 4: The storage unit stores improvement suggestions made by the proposal unit and continuously learns from them. Specifically, it incorporates new technologies and tools to make improvement suggestions, stores these business process improvement suggestions in a database, and continuously learns from them using AI.

[0103] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0104] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0105] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0106] Each of the multiple elements described above, including the analysis unit, proposal unit, sharing unit, and storage unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the analysis unit is implemented by the computer 36 of the smart device 14 and the processor 28 of the data processing unit 12, which automatically collects business flows and analyzes them using AI. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12, which makes improvement suggestions based on the analyzed business flows. The sharing unit is implemented by the control unit 46A of the smart device 14, which shares improvement suggestions within the group company. The storage unit stores improvement suggestions in the database 24 of the data processing unit 12, which continuously learns using AI. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0107] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0108] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0109] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0110] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0111] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0112] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0113] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0114] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0115] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0116] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0117] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0118] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0119] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0120] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0121] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0122] Each of the multiple elements described above, including the analysis unit, proposal unit, sharing unit, and storage unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the analysis unit is implemented by the computer 36 of the smart glasses 214 and the processor 28 of the data processing unit 12, which automatically collects business flows and analyzes them using AI. The proposal unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which makes improvement suggestions based on the analyzed business flows. The sharing unit is implemented, for example, by the control unit 46A of the smart glasses 214, which shares improvement suggestions within the group company. The storage unit stores improvement suggestions in, for example, the database 24 of the data processing unit 12 and continuously learns using AI. The correspondence between each unit and the devices and control units is not limited to the example described above, and various changes are possible.

[0123] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0124] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0125] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0126] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0127] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0128] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0129] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0130] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0131] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0132] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0133] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0134] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0135] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0136] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0137] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0138] Each of the multiple elements described above, including the analysis unit, proposal unit, sharing unit, and storage unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the analysis unit is implemented by the computer 36 of the headset terminal 314 and the processor 28 of the data processing unit 12, which automatically collects business flows and analyzes them using AI. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12, which makes improvement suggestions based on the analyzed business flows. The sharing unit is implemented by the control unit 46A of the headset terminal 314, which shares improvement suggestions within the group company. The storage unit stores improvement suggestions in the database 24 of the data processing unit 12, which continuously learns using AI. The correspondence between each unit and the devices and control units is not limited to the example described above, and various changes are possible.

[0139] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0140] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0141] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0142] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0143] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0144] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0145] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0146] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0147] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0148] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0149] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0150] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0151] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0152] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0153] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0154] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0155] Each of the multiple elements described above, including the analysis unit, proposal unit, sharing unit, and storage unit, is implemented in at least one of the following: the robot 414 and the data processing unit 12. For example, the analysis unit is implemented by the computer 36 of the robot 414 and the processor 28 of the data processing unit 12, which automatically collects business flows and analyzes them using AI. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12, which makes improvement suggestions based on the analyzed business flows. The sharing unit is implemented by the control unit 46A of the robot 414, which shares improvement suggestions within the group company. The storage unit stores improvement suggestions in the database 24 of the data processing unit 12, which continuously learns using AI. The correspondence between each unit and the devices and control units is not limited to the example described above, and various changes are possible.

[0156] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0157] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0158] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0159] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0160] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0161] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0162] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0163] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0164] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0165] 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.

[0166] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0167] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0168] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0169] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0170] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0171] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0172] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0173] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0174] (Note 1) The analysis department automatically analyzes business processes, The proposal department makes improvement suggestions based on the business flow analyzed by the aforementioned analysis department, The Sharing Department shares the improvement proposals made by the aforementioned Proposal Department within the group companies, The system includes a storage unit that stores improvement suggestions made by the aforementioned proposal unit and continuously learns from them. A system characterized by the following features. (Note 2) The aforementioned proposal section is, We propose automating the parts of a specific business workflow that are currently done manually. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned shared portion is, Sharing success stories and know-how from other companies. The system described in Appendix 1, characterized by the features described herein. (Note 4) The storage unit is We propose improvements by incorporating new technologies and tools. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned analysis unit is Analyze tasks that involve a lot of routine work, such as data entry and report writing. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned proposal section is, We propose restructuring the workflow. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned analysis unit is We estimate user emotions and adjust the business process analysis method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned analysis unit is When analyzing business processes, we optimize the analysis algorithm by referring to past business data. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned analysis unit is When analyzing business processes, different analysis methods are applied to each type of business process. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned analysis unit is It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit is When analyzing business processes, the geographical distribution of those processes should be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit is When analyzing business processes, refer to relevant business documents to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way improvement suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned proposal section is, When submitting improvement proposals, adjust the level of detail based on the importance of the task. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned proposal section is, When submitting improvement suggestions, different suggestion algorithms are applied depending on the category of the task. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned proposal section is, It estimates the user's emotions and determines the priority of suggestions based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned proposal section is, When submitting improvement proposals, prioritize them based on the submission date of the work. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned proposal section is, When submitting improvement proposals, adjust the order of the proposals based on their relevance to the work. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned shared portion is, It estimates the user's emotions and adjusts how shared information is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned shared portion is, When displaying shared information, the system selects the optimal display method by referring to the user's past operation history. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned shared portion is, It estimates user sentiment and prioritizes shared information based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned shared portion is, When displaying shared information, the system selects the optimal display method considering the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 23) The storage unit is The system estimates the user's emotions and selects stored data based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The storage unit is During data storage, the storage algorithm is optimized by referring to past stored data. The system described in Appendix 1, characterized by the features described herein. (Note 25) The storage unit is It estimates the user's emotions and adjusts the frequency of accumulation based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The storage unit is During data accumulation, the accumulated data is weighted based on the submission timing of the business process flow. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0175] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The analysis department automatically analyzes business processes, The proposal department makes improvement suggestions based on the business flow analyzed by the aforementioned analysis department, The Sharing Department shares the improvement proposals made by the aforementioned Proposal Department within the group companies, The system includes a storage unit that stores improvement suggestions made by the aforementioned proposal unit and continuously learns from them. A system characterized by the following features.

2. The aforementioned proposal section is, We propose automating the parts of a specific business workflow that are currently done manually. The system according to feature 1.

3. The aforementioned shared portion is, Sharing success stories and know-how from other companies. The system according to feature 1.

4. The storage unit is We propose improvements by incorporating new technologies and tools. The system according to feature 1.

5. The aforementioned analysis unit is Analyze tasks that involve a lot of routine work, such as data entry and report writing. The system according to feature 1.

6. The aforementioned proposal section is, We propose restructuring the workflow. The system according to feature 1.

7. The aforementioned analysis unit is We estimate user emotions and adjust the business process analysis method based on the estimated user emotions. The system according to feature 1.

8. The aforementioned analysis unit is When analyzing business processes, we optimize the analysis algorithm by referring to past business data. The system according to feature 1.

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