System

Abstract

An object of the system according to the embodiment is to automate a task based on information collected from each department in a company.SOLUTION: A system includes a collection unit, an analysis unit, a proposal unit, a generation unit, a storage unit, and a reuse unit. The collection part collects information on business from each department in a company. The analysis unit analyzes the information collected by the collection unit. The proposal unit proposes a practical solution based on the analysis result obtained by the analysis unit. The generating section creates a product based on the solution proposed by the proposing section. The storage unit stores the product created by the creation unit. The reuse unit reuses the product stored in the storage unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have room for improvement in terms of effectively utilizing information collected from various departments within a company and automating business processes.

[0005] The system according to the embodiment aims to automate business processes based on information collected from each department within a company. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a proposal unit, a generation unit, a storage unit, and a reuse unit. The collection unit collects information related to business operations from each department within the company. The analysis unit analyzes the information collected by the collection unit. The proposal unit proposes practical solutions based on the analysis results obtained by the analysis unit. The generation unit creates products based on the solutions proposed by the proposal unit. The storage unit stores the products created by the generation unit. The reuse unit reuses the products stored in the storage unit. [Effects of the Invention]

[0007] The system according to the embodiment can automate business processes based on information collected from each department within a company. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) An automation system according to an embodiment of the present invention collects business information from various departments within a company, analyzes it using a generation AI, proposes practical solutions, and creates, stores, and reuses the resulting products. The automation system uses the generation AI to propose practical solutions based on the internal information and stores the resulting products, allowing other people in the company who have the same concerns to reuse them. For example, the automation system collects business information from various departments within a company, such as each department's workflow, past performance, and problems. This information is input into the generation AI. The automation system then uses the generation AI to analyze the collected information and propose practical solutions. The input to the generation AI is the collected information itself, and the generation AI proposes solutions based on that information. For example, the generation AI receives a prompt such as, "Please identify a bottleneck in a specific business flow and propose a solution," and proposes a solution. The automation system then creates specific products based on the solutions proposed by the generation AI, such as work procedures and automation scripts. These products are stored in a shared folder within the company. The automation system then allows other people in the company to reuse the stored products. For example, an employee in another department who is facing the same problem can download the generated artifacts from the shared folder and apply them to their own work. This eliminates the need to solve the same problem multiple times, improving work efficiency. This allows the automation system to quickly automate internal work and improve work efficiency. Furthermore, the reuse of generated artifacts allows internal knowledge to be shared, improving overall productivity. This allows the automation system to quickly automate internal work and improve work efficiency. For example, an employee in another department who is facing the same problem can download the generated artifacts from the shared folder and apply them to their own work. This eliminates the need to solve the same problem multiple times, improving work efficiency. Furthermore, the reuse of generated artifacts allows internal knowledge to be shared, improving overall productivity.

[0029] An automation system according to an embodiment includes a collection unit, an analysis unit, a proposal unit, a generation unit, a storage unit, and a reuse unit. The collection unit collects business-related information from each department within a company. The departments within the company include, but are not limited to, the sales department, development department, and human resources department. The collection unit collects information, such as each department's business flow, past business performance, and problems. The collection unit can also collect information using methods such as questionnaires, interviews, and database extraction. For example, the collection unit collects detailed information about each department's business flow and understands the procedures and details of each step from start to finish of the business. The analysis unit uses a generation AI to analyze the information collected by the collection unit. The analysis can be performed using, but is not limited to, methods such as data mining, statistical analysis, and machine learning algorithms. For example, the analysis unit identifies bottlenecks in a specific business flow based on the collected information. The analysis unit can also propose efficient business procedures based on past business performance. The proposal unit uses a generation AI to propose practical solutions based on the analysis results obtained by the analysis unit. The proposals include, but are not limited to, improvements to business procedures, the introduction of tools, and process automation. For example, the proposal unit proposes efficient business procedures based on the analysis results. The generation unit uses generative AI to create products based on the solutions proposed by the proposal unit. Products include, but are not limited to, business procedure manuals, automation scripts, and reports. For example, the generation unit creates business procedure manuals that include detailed procedure information, diagrams, checklists, etc. The generation unit can also create automation scripts and clarify the programming language and script functions used. The storage unit stores the products created by the generation unit in a shared folder within the company. Examples of shared folders include, but are not limited to, specific directories on an internal server or cloud storage. For example, the storage unit saves the products in a database and uploads them to the shared folder. The reuse unit reuses the products stored in the storage unit. Reuse can be achieved, for example, by using them in other projects or applying them in other departments, but is not limited to, examples.For example, the reuse unit downloads the generated results from the shared folder and applies them to its own work. This allows the automation system according to the embodiment to quickly automate internal work and improve work efficiency by reusing the generated results. For example, this eliminates the need to repeatedly solve the same problem, improving work efficiency. Furthermore, reusing the generated results allows knowledge to be shared within the company, improving overall productivity.

[0030] The collection unit can collect information including each department's business flow, past business performance, and problems. For example, the collection unit collects detailed information about each department's business flow. For example, it understands the procedures from start to finish and details of each step. The collection unit can also collect past business performance to understand the results of past projects and examples of success and failure of business. For example, the collection unit can identify areas for improvement and success factors for business based on past business performance. The collection unit can also collect problems in each department and identify issues such as business delays, resource shortages, and quality declines. For example, the collection unit can propose business improvement measures based on the problems. By collecting detailed business information from each department, more accurate analysis and proposals are possible. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input information such as each department's business flow, past business performance, and problems into the generation AI and have the generation AI collect information.

[0031] The analysis unit can analyze the collected information and identify bottlenecks in a specific business flow. The analysis unit, for example, identifies bottlenecks in a specific business flow based on the collected information. For example, it identifies steps with long processing times or areas where resources are concentrated. The analysis unit can also use data mining technology to analyze business flow patterns and identify bottlenecks. For example, the analysis unit clusters business flow data and identifies bottleneck steps. The analysis unit can also predict bottlenecks in a business flow using a machine learning algorithm. For example, the analysis unit learns past business data and predicts bottleneck steps. This enables efficient business improvement by identifying bottlenecks in the business flow. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the analysis unit can input the collected information into a generation AI and have the generation AI identify bottlenecks.

[0032] The proposal unit can propose efficient business procedures based on the analysis results. The proposal unit, for example, proposes efficient business procedures based on the analysis results. For example, it proposes simplification of procedures, introduction of tools, automation of processes, etc. The proposal unit can also specifically indicate improvements to the business procedures. For example, the proposal unit aims to improve business efficiency by omitting specific steps. The proposal unit can also propose automation of business by introducing tools. For example, the proposal unit proposes tools for automating specific tasks. In this way, efficient business procedures can be proposed, thereby improving business efficiency. Some or all of the above-mentioned processing in the proposal unit may be performed using, or without, a generation AI. For example, the proposal unit can input the analysis results into a generation AI and have the generation AI execute a proposal for an efficient business procedure.

[0033] The generation unit can create a product including a business procedure manual and an automation script. The generation unit, for example, creates a business procedure manual. For example, the manual includes detailed procedure information, diagrams, checklists, etc. The generation unit can also create an automation script. For example, the generation unit clarifies the programming language to be used and the functions of the script. Furthermore, the generation unit can also create reports. For example, the generation unit creates reports that report the progress and results of the work. This creates a specific product, thereby realizing the automation of the work. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can cause a generation AI to create the business procedure manual and the automation script.

[0034] The storage unit can store the product in a shared folder within the company. For example, the storage unit stores the product in a specific directory on an internal server. For example, the storage unit saves the product in a database and uploads it to a shared folder. The storage unit can also store the product in cloud storage. For example, the storage unit can upload the product to cloud storage so that other employees in the company can access it. By storing the product in a shared folder, it can be reused by other employees. Some or all of the above-mentioned processing in the storage unit may be performed using AI, for example, or may be performed without using AI. For example, the storage unit can input the product into a generation AI and have the generation AI perform the storage processing.

[0035] The reuse unit downloads the generated product from the shared folder so that other employees can reuse it. The reuse unit, for example, downloads the generated product from the shared folder. For example, the reuse unit downloads the generated product to apply it to their own work. The reuse unit can also reuse the generated product in other projects. For example, an employee in another department who faces the same problem downloads the generated product from the shared folder and applies it to their own work. The reuse unit can also apply the generated product in other departments. For example, the reuse unit downloads the generated product and applies it to the work of the other department. This reduces the effort of solving the same problem multiple times, thereby improving work efficiency. Some or all of the above-mentioned processing in the reuse unit may be performed, for example, using AI, or may be performed without using AI. For example, the reuse unit can input the generated product from the shared folder into the generation AI and have the generation AI execute the reuse processing.

[0036] The collection unit can analyze each department's past business performance and select the optimal information collection method. The collection unit, for example, analyzes each department's past business performance. For example, it analyzes past project results and business successes and failures. The collection unit can also select the most effective information collection method based on past business performance. For example, it selects methods such as questionnaires, interviews, and extraction from a database. Furthermore, the collection unit can optimize the timing and frequency of information collection based on each department's business performance. For example, it adjusts the frequency of information collection to achieve efficient information collection. This enables efficient information collection by selecting the optimal information collection method based on past business performance. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input past business performance data into a generation AI and have the generation AI select the optimal information collection method.

[0037] When collecting information, the collection unit can filter the information based on each department's current projects and areas of interest. For example, the collection unit filters information based on each department's current projects and areas of interest. For example, the collection unit collects only information related to ongoing projects. The collection unit can also prioritize collecting necessary information based on each department's areas of interest. For example, the collection unit can prioritize collecting information related to a specific topic. Furthermore, the collection unit can adjust the scope of information collection depending on the progress of each department's project. For example, the collection unit can expand or narrow the scope of information collection depending on the progress of the project. This allows for prioritized collection of necessary information by filtering information based on each department's current projects and areas of interest. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input each department's project information and area of ​​interest data into the generation AI and have the generation AI perform information filtering.

[0038] When collecting information, the collection unit can select the optimal collection means according to the input method of each department. The collection unit, for example, selects the optimal collection means according to the input method of each department. For example, for departments that prefer voice input, information can be collected using voice recognition technology. For departments that prefer text input, information can be collected using text analysis technology. Furthermore, for departments that prefer image input, information can be collected using image analysis technology. For example, image data can be analyzed to extract the necessary information. This enables efficient information collection by selecting the optimal collection means according to the input method of each department. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the input data of each department into a generation AI and have the generation AI select the optimal collection means.

[0039] When collecting information, the collection unit can prioritize collecting highly relevant information by taking into account the geographical location information of each department. The collection unit, for example, collects information by taking into account the geographical location information of each department. For example, it prioritizes collecting information related to a region based on the location of each department. The collection unit can also prioritize collecting nearby information by taking into account the geographical location information of each department. Furthermore, the collection unit can prioritize collecting information related to issues specific to the region based on the geographical location information of each department. For example, it collects information related to specific issues in the region. In this way, by taking the geographical location information into account, it is possible to prioritize collecting information related to the region. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input geographical location information to the generation AI and cause the generation AI to collect information.

[0040] When collecting information, the collection unit can analyze the social media activities of each department and collect specific information. The collection unit, for example, analyzes the social media activities of each department. For example, it analyzes the content of social media posts and collects related information. The collection unit can also measure the social media engagement of each department and collect necessary information. For example, it analyzes reactions and comments on posts and collects related information. Furthermore, the collection unit can analyze the social media activity history of each department and collect related information. For example, it analyzes the content of past posts and collects related information. In this way, related information can be efficiently collected by analyzing social media activity. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input social media data into a generation AI and have the generation AI collect information.

[0041] When collecting information, the collection unit can customize the collection method by reflecting past feedback from each department. For example, the collection unit improves the information collection method based on past feedback from each department. For example, the collection unit customizes the means of information collection based on past survey results and user comments. The collection unit can also customize the means of information collection by reflecting feedback from each department. For example, the collection unit improves the means of information collection based on past feedback. Furthermore, the collection unit can analyze past feedback from each department and select the optimal information collection method. For example, the collection unit selects the optimal information collection method based on past feedback. In this way, the optimal information collection method can be selected by reflecting past feedback. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input past feedback data into a generation AI and have the generation AI customize the information collection method.

[0042] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the information. The analysis unit adjusts the level of detail of the analysis based on, for example, the importance of the information. For example, detailed analysis is performed on information with high importance. Also, simplified analysis can be performed on information with low importance. Furthermore, the analysis unit can adjust the depth of the analysis according to the importance of the information. For example, detailed analysis is performed on information with high importance, and simplified analysis is performed on information with low importance. This enables efficient analysis by adjusting the level of detail of the analysis according to the importance of the information. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input information importance data to the generation AI and have the generation AI adjust the level of detail of the analysis.

[0043] During analysis, the analysis unit can apply different analysis algorithms depending on the category of information. The analysis unit applies different analysis algorithms depending on, for example, the category of information. For example, a process mining algorithm can be applied to information about business flows. A statistical analysis algorithm can also be applied to information about past business performance. A text mining algorithm can also be applied to information about problems. For example, a process mining algorithm can be applied to business flow data, and a statistical analysis algorithm can be applied to past business performance data. This improves the accuracy of analysis by applying an appropriate analysis algorithm depending on the category of information. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input information category data into a generation AI and have the generation AI apply the analysis algorithm.

[0044] During analysis, the analysis unit can improve the accuracy of the analysis by referring to past analysis results from each department. The analysis unit, for example, refers to past analysis results from each department. For example, the analysis algorithm is optimized based on past data sets and analysis reports. The analysis unit can also improve the accuracy of the analysis by referring to past analysis results from each department. Furthermore, the analysis unit can analyze past analysis results from each department and identify areas for improvement in the analysis. For example, the analysis unit improves the accuracy of the analysis based on past analysis results. In this way, the accuracy of the analysis is improved by referring to past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input past analysis result data into the generation AI and have the generation AI improve the accuracy of the analysis.

[0045] During analysis, the analysis unit can determine the priority of analysis based on the time when the information was submitted. The analysis unit determines the priority of analysis based on, for example, the time when the information was submitted. For example, the most recent information is analyzed first. Information that was submitted earlier can also be analyzed later. Furthermore, the analysis unit can adjust the order of analysis based on the time of submission. For example, the order of analysis is adjusted based on the time of submission. In this way, by determining the priority of analysis based on the time when the information was submitted, the most recent information can be analyzed first. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input information submission time data into the generation AI and have the generation AI determine the priority of analysis.

[0046] The analysis unit can adjust the order of analysis based on the relevance of information during analysis. The analysis unit adjusts the order of analysis based on, for example, the relevance of information. For example, highly relevant information is analyzed preferentially. Also, less relevant information can be analyzed later. Furthermore, the analysis unit can adjust the order of analysis based on the relevance of information. For example, the analysis unit adjusts the order of analysis based on the relevance of information. In this way, by adjusting the order of analysis based on the relevance of information, highly relevant information can be analyzed preferentially. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input information relevance data into the generation AI and have the generation AI adjust the order of analysis.

[0047] During analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the expertise level of each department. The analysis unit, for example, adjusts the use of technical terminology in the analysis according to the expertise level of each department. For example, the analysis unit can provide analysis results that use a lot of technical terminology to departments with high expertise. It can also provide concise and easy-to-understand analysis results to departments with low expertise. Furthermore, the analysis unit can adjust the way in which the analysis results are expressed according to the expertise level of each department. For example, it can provide analysis results that use a lot of technical terminology to departments with high expertise, and provide concise and easy-to-understand analysis results to departments with low expertise. In this way, by adjusting the use of technical terminology in the analysis according to the expertise level of each department, it is possible to provide easy-to-understand analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input expertise level data of each department into the generation AI and have the generation AI execute the use of technical terminology in the analysis.

[0048] The proposal unit can adjust the level of detail of the proposal based on the importance of the solution when making a proposal. The proposal unit, for example, adjusts the level of detail of the proposal based on the importance of the solution. For example, a detailed proposal is made for a solution with high importance. Also, a simplified proposal can be made for a solution with low importance. Furthermore, the proposal unit can adjust the depth of the proposal according to the importance of the solution. For example, a detailed proposal is made for a solution with high importance, and a simplified proposal is made for a solution with low importance. In this way, by adjusting the level of detail of the proposal according to the importance of the solution, efficient proposals can be made. Some or all of the above-mentioned processing in the proposal unit may be performed using, or without, a generation AI. For example, the proposal unit can input solution importance data into the generation AI and cause the generation AI to adjust the level of detail of the proposal.

[0049] When making a proposal, the proposal unit can apply different proposal algorithms depending on the category of the solution. For example, the proposal unit applies different proposal algorithms depending on the category of the solution. For example, a process mining algorithm can be applied to solutions related to business flows. A statistical analysis algorithm can also be applied to solutions related to past business performance. A text mining algorithm can also be applied to solutions related to problems. For example, a process mining algorithm can be applied to business flow data, and a statistical analysis algorithm can be applied to past business performance data. This improves the accuracy of the proposal by applying an appropriate proposal algorithm depending on the category of the solution. Some or all of the above-mentioned processing in the proposal unit may be performed using, or without, a generation AI. For example, the proposal unit can input solution category data into the generation AI and cause the generation AI to apply the proposal algorithm.

[0050] When making a proposal, the proposal unit can improve the accuracy of the proposal by referring to past proposal results of each department. The proposal unit, for example, refers to past proposal results of each department. For example, the proposal algorithm is optimized based on past proposal reports and evaluation results. The proposal unit can also improve the accuracy of the proposal by referring to past proposal results of each department. Furthermore, the proposal unit can analyze past proposal results of each department and identify areas for improvement in the proposal. For example, the proposal unit improves the accuracy of the proposal based on past proposal results. In this way, the accuracy of the proposal is improved by referring to past proposal results. Some or all of the above-mentioned processing in the proposal unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the proposal unit can input past proposal result data into the generation AI and have the generation AI improve the accuracy of the proposal.

[0051] When making a proposal, the proposal unit can determine the priority of the proposals based on the submission time of the solutions. The proposal unit, for example, determines the priority of the proposals based on the submission time of the solutions. For example, the most recent solutions are proposed preferentially. Also, solutions that were submitted earlier can be proposed later. Furthermore, the proposal unit can adjust the order of proposals based on the submission time. For example, the order of proposals is adjusted based on the submission time. In this way, by determining the priority of proposals based on the submission time of the solutions, the most recent solutions can be proposed preferentially. Some or all of the above-mentioned processing in the proposal unit may be performed using, or without, the generation AI. For example, the proposal unit can input solution submission time data into the generation AI and have the generation AI determine the priority of the proposals.

[0052] The suggestion unit can adjust the order of proposals based on the relevance of the solutions when proposing them. The suggestion unit, for example, adjusts the order of proposals based on the relevance of the solutions. For example, highly relevant solutions are preferentially proposed. Also, solutions with low relevance can be proposed later. Furthermore, the suggestion unit can adjust the order of proposals based on the relevance of the solutions. For example, the suggestion unit adjusts the order of proposals based on the relevance of the solutions. In this way, by adjusting the order of proposals based on the relevance of the solutions, highly relevant solutions can be preferentially proposed. Some or all of the above-described processing in the suggestion unit may be performed using, or without, the generation AI. For example, the suggestion unit can input relevance data of the solutions into the generation AI and cause the generation AI to adjust the order of proposals.

[0053] When making a proposal, the suggestion unit can adjust the use of technical terminology in the proposal according to the expertise level of each department. The suggestion unit, for example, adjusts the use of technical terminology in the proposal according to the expertise level of each department. For example, the suggestion unit can provide proposals that use a lot of technical terminology to departments with high expertise. It can also provide concise and easy-to-understand proposals to departments with low expertise. Furthermore, the suggestion unit can adjust the way the proposal is expressed according to the expertise level of each department. For example, the suggestion unit can provide proposals that use a lot of technical terminology to departments with high expertise, and provide concise and easy-to-understand proposals to departments with low expertise. By adjusting the use of technical terminology in the proposal according to the expertise level of each department, it is possible to provide easy-to-understand proposals. Some or all of the above-described processing in the suggestion unit may be performed using, or without, a generation AI. For example, the suggestion unit can input expertise level data of each department into the generation AI and cause the generation AI to execute the use of technical terminology in the proposal.

[0054] The generation unit can adjust the level of detail of the generation based on the importance of the product during generation. The generation unit adjusts the level of detail of the generation based on, for example, the importance of the product. For example, detailed generation is performed for products with high importance. Also, simplified generation can be performed for products with low importance. Furthermore, the generation unit can adjust the depth of generation according to the importance of the product. For example, detailed generation is performed for products with high importance, and simplified generation is performed for products with low importance. In this way, efficient generation is possible by adjusting the level of detail of generation according to the importance of the product. Some or all of the above-mentioned processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input product importance data to the generation AI and cause the generation AI to adjust the level of detail of generation.

[0055] The generation unit can apply different generation algorithms depending on the category of the product during generation. The generation unit applies different generation algorithms depending on, for example, the category of the product. For example, a text generation algorithm can be applied to a business procedure manual. A code generation algorithm can also be applied to an automation script. A slide generation algorithm can also be applied to presentation materials. For example, a text generation algorithm can be applied to a business procedure manual, and a code generation algorithm can be applied to an automation script. This improves the accuracy of generation by applying an appropriate generation algorithm depending on the category of the product. Some or all of the above-mentioned processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input product category data into the generation AI and cause the generation AI to apply the generation algorithm.

[0056] During generation, the generation unit can improve the accuracy of generation by referring to past generation results of each department. The generation unit, for example, refers to past generation results of each department. For example, the generation algorithm is optimized based on past generation reports and evaluation results. The generation unit can also improve the accuracy of generation by referring to past generation results of each department. Furthermore, the generation unit can analyze past generation results of each department and identify areas for improvement in generation. For example, the generation unit improves the accuracy of generation based on past generation results. In this way, the accuracy of generation is improved by referring to past generation results. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input past generation result data into the generation AI and cause the generation AI to improve the accuracy of generation.

[0057] At the time of generation, the generation unit can determine the generation priority based on the submission time of the product. The generation unit determines the generation priority based on, for example, the submission time of the product. For example, the newest product is generated preferentially. Also, products submitted earlier can be generated later. Furthermore, the generation unit can adjust the order of generation based on the submission time. For example, the order of generation is adjusted based on the submission time. In this way, by determining the generation priority based on the submission time of the product, the newest product can be generated preferentially. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input product submission time data into the generation AI and cause the generation AI to determine the generation priority.

[0058] The generation unit can adjust the order of generation based on the relevance of the products during generation. The generation unit adjusts the order of generation based on, for example, the relevance of the products. For example, highly relevant products are generated preferentially. Also, products with low relevance can be generated later. Furthermore, the generation unit can adjust the order of generation based on the relevance of the products. For example, the generation order is adjusted based on the relevance of the products. In this way, by adjusting the order of generation based on the relevance of the products, highly relevant products can be generated preferentially. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit inputs product relevance data into the generation AI and causes the generation AI to adjust the order of generation.

[0059] During generation, the generation unit can adjust the use of technical terminology in the product according to the expertise level of each department. The generation unit, for example, adjusts the use of technical terminology in the product according to the expertise level of each department. For example, it can provide a product that uses a lot of technical terminology to departments with high expertise. It can also provide a concise and easy-to-understand product to departments with low expertise. Furthermore, the generation unit can adjust the expression method of the product according to the expertise level of each department. For example, it can provide a product that uses a lot of technical terminology to departments with high expertise, and a concise and easy-to-understand product to departments with low expertise. In this way, by adjusting the use of technical terminology in the product according to the expertise level of each department, it is possible to provide a product that is easy to understand. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input expertise level data of each department into the generation AI and cause the generation AI to execute the use of technical terminology in the product.

[0060] The storage unit can adjust the level of storage detail based on the importance of the product when storing. The storage unit adjusts the level of storage detail based on, for example, the importance of the product. For example, detailed storage is performed for products with high importance. Also, simplified storage can be performed for products with low importance. Furthermore, the storage unit can adjust the depth of storage according to the importance of the product. For example, detailed storage is performed for products with high importance, and simplified storage is performed for products with low importance. In this way, efficient storage is possible by adjusting the level of storage detail according to the importance of the product. Some or all of the above-mentioned processing in the storage unit may be performed using, for example, AI, or may be performed without using AI. For example, the storage unit can input product importance data to the generation AI and cause the generation AI to adjust the level of storage detail.

[0061] The storage unit can apply different storage algorithms depending on the category of the product when storing. The storage unit applies different storage algorithms depending on, for example, the category of the product. For example, a text storage algorithm can be applied to a business procedure manual. A code storage algorithm can also be applied to an automation script. A slide storage algorithm can also be applied to presentation materials. For example, a text storage algorithm can be applied to a business procedure manual, and a code storage algorithm can be applied to an automation script. This improves the accuracy of storage by applying an appropriate storage algorithm depending on the category of the product. Some or all of the above-mentioned processing in the storage unit may be performed using, for example, AI, or may be performed without using AI. For example, the storage unit can input product category data to a generation AI and cause the generation AI to apply a storage algorithm.

[0062] The storage unit can improve the accuracy of storage by referring to the past storage results of each department when storing data. The storage unit, for example, refers to the past storage results of each department. For example, the storage algorithm is optimized based on past storage reports and evaluation results. The storage unit can also improve the accuracy of storage by referring to the past storage results of each department. Furthermore, the storage unit can analyze the past storage results of each department and identify areas for improvement in storage. For example, the storage unit improves the accuracy of storage based on the past storage results. In this way, the accuracy of storage is improved by referring to the past storage results. Some or all of the above-mentioned processing in the storage unit may be performed using, for example, AI, or may be performed without using AI. For example, the storage unit can input past storage result data into a generation AI and have the generation AI improve the accuracy of storage.

[0063] The storage unit can determine the storage priority based on the submission time of the product when storing. The storage unit determines the storage priority based on, for example, the submission time of the product. For example, the most recent product is stored preferentially. Also, products submitted earlier can be stored later. Furthermore, the storage unit can adjust the storage order based on the submission time. For example, the storage order is adjusted based on the submission time. In this way, by determining the storage priority based on the submission time of the product, the most recent product can be stored preferentially. Some or all of the above-mentioned processing in the storage unit may be performed using, for example, AI, or may be performed without using AI. For example, the storage unit can input product submission time data to the generation AI and have the generation AI determine the storage priority.

[0064] The storage unit can adjust the storage order based on the relevance of the products when storing them. The storage unit adjusts the storage order based on, for example, the relevance of the products. For example, highly relevant products are stored preferentially. Also, products with low relevance can be stored later. Furthermore, the storage unit can adjust the storage order based on the relevance of the products. For example, the storage order is adjusted based on the relevance of the products. In this way, by adjusting the storage order based on the relevance of the products, highly relevant products can be stored preferentially. Some or all of the above-mentioned processing in the storage unit may be performed using, for example, AI, or may be performed without using AI. For example, the storage unit can input product relevance data to a generation AI and cause the generation AI to adjust the storage order.

[0065] The storage unit can adjust the use of technical terms in the stored content according to the expertise level of each department when storing the content. The storage unit, for example, adjusts the use of technical terms in the stored content according to the expertise level of each department. For example, it can provide stored content that uses a lot of technical terms to departments with high expertise. It can also provide concise and easy-to-understand stored content to departments with low expertise. Furthermore, the storage unit can adjust the way the stored content is expressed according to the expertise level of each department. For example, it can provide stored content that uses a lot of technical terms to departments with high expertise, and provide concise and easy-to-understand stored content to departments with low expertise. In this way, by adjusting the use of technical terms in the stored content according to the expertise level of each department, it is possible to provide easy-to-understand stored content. Some or all of the above-mentioned processing in the storage unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the storage unit can input expertise level data of each department into a generation AI and have the generation AI execute the use of technical terms in the stored content.

[0066] The reuse unit can adjust the level of detail of reuse based on the importance of the product during reuse. The reuse unit adjusts the level of detail of reuse based on, for example, the importance of the product. For example, detailed reuse is performed for products with high importance. Also, simplified reuse can be performed for products with low importance. Furthermore, the reuse unit can adjust the depth of reuse according to the importance of the product. For example, detailed reuse is performed for products with high importance, and simplified reuse is performed for products with low importance. In this way, efficient reuse is possible by adjusting the level of detail of reuse according to the importance of the product. Some or all of the above-mentioned processing in the reuse unit may be performed using, for example, AI, or may be performed without using AI. For example, the reuse unit can input product importance data to the generation AI and cause the generation AI to adjust the level of detail of reuse.

[0067] The reuse unit can apply different reuse algorithms depending on the category of the product during reuse. For example, the reuse unit applies different reuse algorithms depending on the category of the product. For example, a text reuse algorithm can be applied to a business procedure manual. A code reuse algorithm can also be applied to an automation script. A slide reuse algorithm can also be applied to presentation materials. For example, a text reuse algorithm can be applied to a business procedure manual, and a code reuse algorithm can be applied to an automation script. This improves the accuracy of reuse by applying an appropriate reuse algorithm depending on the category of the product. Some or all of the above-mentioned processing in the reuse unit may be performed using, or without, a generation AI. For example, the reuse unit can input product category data into the generation AI and cause the generation AI to apply the reuse algorithm.

[0068] During reuse, the reuse unit can improve the accuracy of reuse by referring to the past reuse results of each department. The reuse unit, for example, refers to the past reuse results of each department. For example, the reuse algorithm is optimized based on past reuse reports and evaluation results. The reuse unit can also improve the accuracy of reuse by referring to the past reuse results of each department. Furthermore, the reuse unit can analyze the past reuse results of each department and identify areas for improvement in reuse. For example, the reuse unit improves the accuracy of reuse based on the past reuse results. In this way, the accuracy of reuse is improved by referring to the past reuse results. Some or all of the above-mentioned processing in the reuse unit may be performed, for example, using AI, or may be performed without using AI. For example, the reuse unit can input past reuse result data into the generation AI and have the generation AI improve the accuracy of reuse.

[0069] At the time of reuse, the reuse unit can determine the priority of reuse based on the submission time of the product. The reuse unit determines the priority of reuse based on, for example, the submission time of the product. For example, the most recent product is reused preferentially. Also, products submitted earlier can be reused later. Furthermore, the reuse unit can adjust the order of reuse based on the submission time. For example, the order of reuse is adjusted based on the submission time. In this way, by determining the priority of reuse based on the submission time of the product, the most recent product can be reused preferentially. Some or all of the above-mentioned processing in the reuse unit may be performed using, for example, AI, or may be performed without using AI. For example, the reuse unit can input product submission time data into the generation AI and cause the generation AI to determine the priority of reuse.

[0070] The reuse unit can adjust the order of reuse based on the relevance of the products during reuse. The reuse unit adjusts the order of reuse based on, for example, the relevance of the products. For example, highly relevant products are reused preferentially. Also, products with low relevance can be reused later. Furthermore, the reuse unit can also adjust the order of reuse based on the relevance of the products. For example, the order of reuse is adjusted based on the relevance of the products. In this way, by adjusting the order of reuse based on the relevance of the products, highly relevant products can be reused preferentially. Some or all of the above-mentioned processing in the reuse unit may be performed using, for example, AI, or may be performed without using AI. For example, the reuse unit inputs product relevance data to the generation AI and causes the generation AI to adjust the order of reuse.

[0071] During reuse, the reuse unit can adjust the use of technical terminology in the reused material according to the expertise level of each department. The reuse unit, for example, adjusts the use of technical terminology in the reused material according to the expertise level of each department. For example, it can provide reused material that uses a lot of technical terminology to departments with high expertise. It can also provide reused material that is concise and easy to understand to departments with low expertise. Furthermore, the reuse unit can adjust the way the reused material is expressed according to the expertise level of each department. For example, it can provide reused material that uses a lot of technical terminology to departments with high expertise, and provide reused material that is concise and easy to understand to departments with low expertise. By adjusting the use of technical terminology in the reused material according to the expertise level of each department, it is possible to provide reused material that is easy to understand. Some or all of the above-described processing in the reuse unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the reuse unit can input expertise level data of each department into the generation AI and have the generation AI execute the use of technical terminology in the reused material.

[0072] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0073] When collecting information on each department's work flow, past work performance, and problems, the collection unit can also adjust the timing of information collection taking into account each department's busy and slow periods. For example, the collection unit can reduce the frequency of information collection during busy periods and collect detailed information during slow periods. The timing of information collection can also be adjusted depending on the progress of a specific project. This reduces the workload and enables efficient information collection. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input work schedule data for each department into the generation AI and have the generation AI adjust the timing of information collection.

[0074] When creating a product, the generation unit can also adjust the format of the product according to the characteristics of each department's work. For example, it can provide detailed technical specifications to the engineering department and simple presentation materials to the sales department. It can also customize the content of the product according to each department's work flow. This can improve work efficiency by providing products that meet the needs of each department. Some or all of the above-mentioned processing in the generation unit can be performed using, for example, AI, or can be performed without using AI. For example, the generation unit can input business data from each department into the generation AI and have the generation AI adjust the format of the product.

[0075] When selecting artifacts to reuse, the reuse department can select the most appropriate artifacts by taking into consideration the characteristics of each department's work and the progress of the current project. For example, the reuse department can provide artifacts containing technical details to the engineering department and presentation materials to the sales department. It can also provide artifacts related to ongoing projects preferentially. This allows for the reuse of artifacts that meet the needs of each department, thereby improving work efficiency. Some or all of the above-mentioned processing in the reuse department can be performed using, or without, AI. For example, the reuse department can input business data and project data from each department into a generation AI and have the generation AI select the most appropriate artifacts.

[0076] When providing analysis results, the analysis unit can provide optimal analysis results by taking into account the characteristics of each department's work and the progress of current projects. For example, analysis results including technical details can be provided to the engineering department, and concise analysis results can be provided to the sales department. Analysis results related to ongoing projects can also be provided preferentially. This allows for the provision of analysis results tailored to the needs of each department, thereby improving work efficiency. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input business data and project data from each department into the generation AI and have the generation AI provide optimal analysis results.

[0077] When creating a product, the generation unit can also improve the accuracy of generation by referring to past generation results from each department. For example, the generation algorithm can be optimized based on past generation reports and evaluation results. The generation unit can also analyze past generation results from each department to identify areas for improvement in generation. In this way, by referring to past generation results, the accuracy of generation can be improved. Some or all of the above-mentioned processing in the generation unit can be performed using, for example, AI, or can be performed without using AI. For example, the generation unit can input past generation result data into the generation AI and have the generation AI improve the accuracy of generation.

[0078] The processing flow of the first embodiment will be briefly explained below.

[0079] Step 1: The collection department collects information about operations from each department within the company. These departments include the sales department, development department, and human resources department. The collection department collects information about each department's business flow, past business performance, problems, etc. Methods of collection include questionnaires, interviews, and extraction from databases. Step 2: The analysis unit uses the generation AI to analyze the information collected by the collection unit. Analysis methods include data mining, statistical analysis, and machine learning algorithms. The analysis unit identifies bottlenecks in specific business processes based on the collected information and proposes efficient business procedures based on past business performance. Step 3: The Proposal Department uses generative AI to propose practical solutions based on the analysis results obtained by the Analysis Department. Suggestions include improving business procedures, introducing tools, and automating processes. The Proposal Department proposes efficient business procedures based on the analysis results. Step 4: The generation department uses the generation AI to create products based on the solutions proposed by the proposal department. Products include work procedures, automation scripts, reports, etc. The generation department creates work procedures, including detailed procedures, diagrams, checklists, etc. It also creates automation scripts, clarifying the programming language used and the script's functions. Step 5: The storage unit stores the product created by the generation unit in a shared folder within the company. The shared folder can be a specific directory on an internal server or cloud storage. The storage unit saves the product in a database and uploads it to the shared folder. Step 6: The reuse department reuses the artifacts stored in the repository. Reuse methods include using them in other projects or applying them to other departments. The reuse department downloads the artifacts from the shared folder and applies them to their own work.

[0080] (Example 2) An automation system according to an embodiment of the present invention collects business information from various departments within a company, analyzes it using a generation AI, proposes practical solutions, and creates, stores, and reuses the resulting products. The automation system uses the generation AI to propose practical solutions based on the internal information and stores the resulting products, allowing other people in the company who have the same concerns to reuse them. For example, the automation system collects business information from various departments within a company, such as each department's workflow, past performance, and problems. This information is input into the generation AI. The automation system then uses the generation AI to analyze the collected information and propose practical solutions. The input to the generation AI is the collected information itself, and the generation AI proposes solutions based on that information. For example, the generation AI receives a prompt such as, "Please identify a bottleneck in a specific business flow and propose a solution," and proposes a solution. The automation system then creates specific products based on the solutions proposed by the generation AI, such as work procedures and automation scripts. These products are stored in a shared folder within the company. The automation system then allows other people in the company to reuse the stored products. For example, an employee in another department who is facing the same problem can download the generated artifacts from the shared folder and apply them to their own work. This eliminates the need to solve the same problem multiple times, improving work efficiency. This allows the automation system to quickly automate internal work and improve work efficiency. Furthermore, the reuse of generated artifacts allows internal knowledge to be shared, improving overall productivity. This allows the automation system to quickly automate internal work and improve work efficiency. For example, an employee in another department who is facing the same problem can download the generated artifacts from the shared folder and apply them to their own work. This eliminates the need to solve the same problem multiple times, improving work efficiency. Furthermore, the reuse of generated artifacts allows internal knowledge to be shared, improving overall productivity.

[0081] An automation system according to an embodiment includes a collection unit, an analysis unit, a proposal unit, a generation unit, a storage unit, and a reuse unit. The collection unit collects business-related information from each department within a company. The departments within the company include, but are not limited to, the sales department, development department, and human resources department. The collection unit collects information, such as each department's business flow, past business performance, and problems. The collection unit can also collect information using methods such as questionnaires, interviews, and database extraction. For example, the collection unit collects detailed information about each department's business flow and understands the procedures and details of each step from start to finish of the business. The analysis unit uses a generation AI to analyze the information collected by the collection unit. The analysis can be performed using, but is not limited to, methods such as data mining, statistical analysis, and machine learning algorithms. For example, the analysis unit identifies bottlenecks in a specific business flow based on the collected information. The analysis unit can also propose efficient business procedures based on past business performance. The proposal unit uses a generation AI to propose practical solutions based on the analysis results obtained by the analysis unit. The proposals include, but are not limited to, improvements to business procedures, the introduction of tools, and process automation. For example, the proposal unit proposes efficient business procedures based on the analysis results. The generation unit uses generative AI to create products based on the solutions proposed by the proposal unit. Products include, but are not limited to, business procedure manuals, automation scripts, and reports. For example, the generation unit creates business procedure manuals that include detailed procedure information, diagrams, checklists, etc. The generation unit can also create automation scripts and clarify the programming language and script functions used. The storage unit stores the products created by the generation unit in a shared folder within the company. Examples of shared folders include, but are not limited to, specific directories on an internal server or cloud storage. For example, the storage unit saves the products in a database and uploads them to the shared folder. The reuse unit reuses the products stored in the storage unit. Reuse can be achieved, for example, by using them in other projects or applying them in other departments, but is not limited to, examples.For example, the reuse unit downloads the generated results from the shared folder and applies them to its own work. This allows the automation system according to the embodiment to quickly automate internal work and improve work efficiency by reusing the generated results. For example, this eliminates the need to repeatedly solve the same problem, improving work efficiency. Furthermore, reusing the generated results allows knowledge to be shared within the company, improving overall productivity.

[0082] The collection unit can collect information including each department's business flow, past business performance, and problems. For example, the collection unit collects detailed information about each department's business flow. For example, it understands the procedures from start to finish and details of each step. The collection unit can also collect past business performance to understand the results of past projects and examples of success and failure of business. For example, the collection unit can identify areas for improvement and success factors for business based on past business performance. The collection unit can also collect problems in each department and identify issues such as business delays, resource shortages, and quality declines. For example, the collection unit can propose business improvement measures based on the problems. By collecting detailed business information from each department, more accurate analysis and proposals are possible. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input information such as each department's business flow, past business performance, and problems into the generation AI and have the generation AI collect information.

[0083] The analysis unit can analyze the collected information and identify bottlenecks in a specific business flow. The analysis unit, for example, identifies bottlenecks in a specific business flow based on the collected information. For example, it identifies steps with long processing times or areas where resources are concentrated. The analysis unit can also use data mining technology to analyze business flow patterns and identify bottlenecks. For example, the analysis unit clusters business flow data and identifies bottleneck steps. The analysis unit can also predict bottlenecks in a business flow using a machine learning algorithm. For example, the analysis unit learns past business data and predicts bottleneck steps. This enables efficient business improvement by identifying bottlenecks in the business flow. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the analysis unit can input the collected information into a generation AI and have the generation AI identify bottlenecks.

[0084] The proposal unit can propose efficient business procedures based on the analysis results. The proposal unit, for example, proposes efficient business procedures based on the analysis results. For example, it proposes simplification of procedures, introduction of tools, automation of processes, etc. The proposal unit can also specifically indicate improvements to the business procedures. For example, the proposal unit aims to improve business efficiency by omitting specific steps. The proposal unit can also propose automation of business by introducing tools. For example, the proposal unit proposes tools for automating specific tasks. In this way, efficient business procedures can be proposed, thereby improving business efficiency. Some or all of the above-mentioned processing in the proposal unit may be performed using, or without, a generation AI. For example, the proposal unit can input the analysis results into a generation AI and have the generation AI execute a proposal for an efficient business procedure.

[0085] The generation unit can create a product including a business procedure manual and an automation script. The generation unit, for example, creates a business procedure manual. For example, the manual includes detailed procedure information, diagrams, checklists, etc. The generation unit can also create an automation script. For example, the generation unit clarifies the programming language to be used and the functions of the script. Furthermore, the generation unit can also create reports. For example, the generation unit creates reports that report the progress and results of the work. This creates a specific product, thereby realizing the automation of the work. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can cause a generation AI to create the business procedure manual and the automation script.

[0086] The storage unit can store the product in a shared folder within the company. For example, the storage unit stores the product in a specific directory on an internal server. For example, the storage unit saves the product in a database and uploads it to a shared folder. The storage unit can also store the product in cloud storage. For example, the storage unit can upload the product to cloud storage so that other employees in the company can access it. By storing the product in a shared folder, it can be reused by other employees. Some or all of the above-mentioned processing in the storage unit may be performed using AI, for example, or may be performed without using AI. For example, the storage unit can input the product into a generation AI and have the generation AI perform the storage processing.

[0087] The reuse unit downloads the generated product from the shared folder so that other employees can reuse it. The reuse unit, for example, downloads the generated product from the shared folder. For example, the reuse unit downloads the generated product to apply it to their own work. The reuse unit can also reuse the generated product in other projects. For example, an employee in another department who faces the same problem downloads the generated product from the shared folder and applies it to their own work. The reuse unit can also apply the generated product in other departments. For example, the reuse unit downloads the generated product and applies it to the work of the other department. This reduces the effort of solving the same problem multiple times, thereby improving work efficiency. Some or all of the above-mentioned processing in the reuse unit may be performed, for example, using AI, or may be performed without using AI. For example, the reuse unit can input the generated product from the shared folder into the generation AI and have the generation AI execute the reuse processing.

[0088] The collection unit can estimate the user's emotions and adjust the timing of information collection based on the estimated user's emotions. The collection unit, for example, estimates the user's emotions. For example, it uses facial expression recognition technology to analyze the user's facial expressions and estimate the emotions. The collection unit can also analyze the user's voice using voice analysis technology to estimate the emotions. For example, it analyzes the tone and speed of the voice and estimates the emotions. The collection unit can also estimate the user's emotions based on survey results. For example, it analyzes the content of the survey answered by the user and estimates the emotions. Next, the collection unit adjusts the timing of information collection based on the estimated user's emotions. For example, if the user is feeling stressed, it can reduce the frequency of information collection to reduce the user's burden. Also, if the user is relaxed, it can increase the frequency of information collection to collect more detailed information. Furthermore, if the user is in a hurry, it can speed up the timing of information collection and collect necessary information immediately. This reduces the user's burden by adjusting the timing of information collection according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using AI, or may be performed without using AI. For example, the collection unit may input user emotion data into the generation AI and cause the generation AI to estimate the emotion.

[0089] The collection unit can analyze each department's past business performance and select the optimal information collection method. The collection unit, for example, analyzes each department's past business performance. For example, it analyzes past project results and business successes and failures. The collection unit can also select the most effective information collection method based on past business performance. For example, it selects methods such as questionnaires, interviews, and extraction from a database. Furthermore, the collection unit can optimize the timing and frequency of information collection based on each department's business performance. For example, it adjusts the frequency of information collection to achieve efficient information collection. This enables efficient information collection by selecting the optimal information collection method based on past business performance. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input past business performance data into a generation AI and have the generation AI select the optimal information collection method.

[0090] When collecting information, the collection unit can filter the information based on each department's current projects and areas of interest. For example, the collection unit filters information based on each department's current projects and areas of interest. For example, the collection unit collects only information related to ongoing projects. The collection unit can also prioritize collecting necessary information based on each department's areas of interest. For example, the collection unit can prioritize collecting information related to a specific topic. Furthermore, the collection unit can adjust the scope of information collection depending on the progress of each department's project. For example, the collection unit can expand or narrow the scope of information collection depending on the progress of the project. This allows for prioritized collection of necessary information by filtering information based on each department's current projects and areas of interest. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input each department's project information and area of ​​interest data into the generation AI and have the generation AI perform information filtering.

[0091] When collecting information, the collection unit can select the optimal collection means according to the input method of each department. The collection unit, for example, selects the optimal collection means according to the input method of each department. For example, for departments that prefer voice input, information can be collected using voice recognition technology. For departments that prefer text input, information can be collected using text analysis technology. Furthermore, for departments that prefer image input, information can be collected using image analysis technology. For example, image data can be analyzed to extract the necessary information. This enables efficient information collection by selecting the optimal collection means according to the input method of each department. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the input data of each department into a generation AI and have the generation AI select the optimal collection means.

[0092] The collection unit can estimate the user's emotions and determine the priority of information to be collected based on the estimated user's emotions. The collection unit, for example, estimates the user's emotions. For example, it uses facial expression recognition technology to analyze the user's facial expressions and estimate the emotions. The collection unit can also analyze the user's voice using voice analysis technology to estimate the emotions. For example, it analyzes the tone and speed of the voice and estimates the emotions. Furthermore, the collection unit can estimate the user's emotions based on survey results. For example, it analyzes the content of the survey answered by the user and estimates the emotions. Next, the collection unit determines the priority of information to be collected based on the estimated user's emotions. For example, if the user is feeling stressed, it can prioritize collecting information of high importance. Also, if the user is relaxed, it can prioritize collecting detailed information. Furthermore, if the user is in a hurry, it can prioritize collecting information that can be collected quickly. In this way, by prioritizing information according to the user's emotions, important information can be prioritized. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using AI, or may be performed without using AI. For example, the collection unit may input user emotion data into the generation AI and have the generation AI determine the priority of the information.

[0093] When collecting information, the collection unit can prioritize collecting highly relevant information by taking into account the geographical location information of each department. The collection unit, for example, collects information by taking into account the geographical location information of each department. For example, it prioritizes collecting information related to a region based on the location of each department. The collection unit can also prioritize collecting nearby information by taking into account the geographical location information of each department. Furthermore, the collection unit can prioritize collecting information related to issues specific to the region based on the geographical location information of each department. For example, it collects information related to specific issues in the region. In this way, by taking the geographical location information into account, it is possible to prioritize collecting information related to the region. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input geographical location information to the generation AI and cause the generation AI to collect information.

[0094] When collecting information, the collection unit can analyze the social media activities of each department and collect specific information. The collection unit, for example, analyzes the social media activities of each department. For example, it analyzes the content of social media posts and collects related information. The collection unit can also measure the social media engagement of each department and collect necessary information. For example, it analyzes reactions and comments on posts and collects related information. Furthermore, the collection unit can analyze the social media activity history of each department and collect related information. For example, it analyzes the content of past posts and collects related information. In this way, related information can be efficiently collected by analyzing social media activity. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input social media data into a generation AI and have the generation AI collect information.

[0095] When collecting information, the collection unit can customize the collection method by reflecting past feedback from each department. For example, the collection unit improves the information collection method based on past feedback from each department. For example, the collection unit customizes the means of information collection based on past survey results and user comments. The collection unit can also customize the means of information collection by reflecting feedback from each department. For example, the collection unit improves the means of information collection based on past feedback. Furthermore, the collection unit can analyze past feedback from each department and select the optimal information collection method. For example, the collection unit selects the optimal information collection method based on past feedback. In this way, the optimal information collection method can be selected by reflecting past feedback. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input past feedback data into a generation AI and have the generation AI customize the information collection method.

[0096] The analysis unit can estimate the user's emotions and adjust the presentation method of the analysis based on the estimated user's emotions. The analysis unit, for example, estimates the user's emotions. For example, it uses facial expression recognition technology to analyze the user's facial expressions and estimate emotions. The analysis unit can also analyze the user's voice using voice analysis technology to estimate emotions. For example, it analyzes the tone and speed of the voice to estimate emotions. Furthermore, the analysis unit can estimate the user's emotions based on survey results. For example, it analyzes the content of the survey answered by the user and estimates emotions. Next, the analysis unit adjusts the presentation method of the analysis based on the estimated user's emotions. For example, if the user is nervous, it can provide simple, highly visible analysis results. On the other hand, if the user is relaxed, it can provide detailed analysis results. Furthermore, if the user is in a hurry, it can provide analysis results that are concise. By adjusting the presentation method of the analysis according to the user's emotions, it is possible to provide analysis results that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using AI, or may be performed without using AI. For example, the analysis unit may input user emotion data into the generation AI and cause the generation AI to adjust the method of expression of the analysis.

[0097] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the information. The analysis unit adjusts the level of detail of the analysis based on, for example, the importance of the information. For example, detailed analysis is performed on information with high importance. Also, simplified analysis can be performed on information with low importance. Furthermore, the analysis unit can adjust the depth of the analysis according to the importance of the information. For example, detailed analysis is performed on information with high importance, and simplified analysis is performed on information with low importance. This enables efficient analysis by adjusting the level of detail of the analysis according to the importance of the information. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input information importance data to the generation AI and have the generation AI adjust the level of detail of the analysis.

[0098] During analysis, the analysis unit can apply different analysis algorithms depending on the category of information. The analysis unit applies different analysis algorithms depending on, for example, the category of information. For example, a process mining algorithm can be applied to information about business flows. A statistical analysis algorithm can also be applied to information about past business performance. A text mining algorithm can also be applied to information about problems. For example, a process mining algorithm can be applied to business flow data, and a statistical analysis algorithm can be applied to past business performance data. This improves the accuracy of analysis by applying an appropriate analysis algorithm depending on the category of information. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input information category data into a generation AI and have the generation AI apply the analysis algorithm.

[0099] During analysis, the analysis unit can improve the accuracy of the analysis by referring to past analysis results from each department. The analysis unit, for example, refers to past analysis results from each department. For example, the analysis algorithm is optimized based on past data sets and analysis reports. The analysis unit can also improve the accuracy of the analysis by referring to past analysis results from each department. Furthermore, the analysis unit can analyze past analysis results from each department and identify areas for improvement in the analysis. For example, the analysis unit improves the accuracy of the analysis based on past analysis results. In this way, the accuracy of the analysis is improved by referring to past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input past analysis result data into the generation AI and have the generation AI improve the accuracy of the analysis.

[0100] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. The analysis unit, for example, estimates the user's emotions. For example, it uses facial expression recognition technology to analyze the user's facial expressions and estimate emotions. The analysis unit can also analyze the user's voice using voice analysis technology to estimate emotions. For example, it analyzes the tone and speed of the voice to estimate emotions. Furthermore, the analysis unit can estimate the user's emotions based on survey results. For example, it analyzes the content of the survey answered by the user and estimates emotions. Next, the analysis unit adjusts the length of the analysis based on the estimated user emotions. For example, if the user is in a hurry, it can provide a short and concise analysis result. Alternatively, if the user is relaxed, it can provide a detailed analysis result. Furthermore, if the user is excited, it can provide a visually stimulating analysis result. By adjusting the length of the analysis according to the user's emotions, it is possible to provide the optimal analysis result for the user. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using AI, or may be performed without using AI. For example, the analysis unit may input user emotion data into the generation AI and cause the generation AI to adjust the length of the analysis.

[0101] During analysis, the analysis unit can determine the priority of analysis based on the time when the information was submitted. The analysis unit determines the priority of analysis based on, for example, the time when the information was submitted. For example, the most recent information is analyzed first. Information that was submitted earlier can also be analyzed later. Furthermore, the analysis unit can adjust the order of analysis based on the time of submission. For example, the order of analysis is adjusted based on the time of submission. In this way, by determining the priority of analysis based on the time when the information was submitted, the most recent information can be analyzed first. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input information submission time data into the generation AI and have the generation AI determine the priority of analysis.

[0102] The analysis unit can adjust the order of analysis based on the relevance of information during analysis. The analysis unit adjusts the order of analysis based on, for example, the relevance of information. For example, highly relevant information is analyzed preferentially. Also, less relevant information can be analyzed later. Furthermore, the analysis unit can adjust the order of analysis based on the relevance of information. For example, the analysis unit adjusts the order of analysis based on the relevance of information. In this way, by adjusting the order of analysis based on the relevance of information, highly relevant information can be analyzed preferentially. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input information relevance data into the generation AI and have the generation AI adjust the order of analysis.

[0103] During analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the expertise level of each department. The analysis unit, for example, adjusts the use of technical terminology in the analysis according to the expertise level of each department. For example, the analysis unit can provide analysis results that use a lot of technical terminology to departments with high expertise. It can also provide concise and easy-to-understand analysis results to departments with low expertise. Furthermore, the analysis unit can adjust the way in which the analysis results are expressed according to the expertise level of each department. For example, it can provide analysis results that use a lot of technical terminology to departments with high expertise, and provide concise and easy-to-understand analysis results to departments with low expertise. In this way, by adjusting the use of technical terminology in the analysis according to the expertise level of each department, it is possible to provide easy-to-understand analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input expertise level data of each department into the generation AI and have the generation AI execute the use of technical terminology in the analysis.

[0104] The suggestion unit can estimate the user's emotions and adjust the way suggestions are presented based on the estimated user's emotions. The suggestion unit, for example, estimates the user's emotions. For example, it uses facial expression recognition technology to analyze the user's facial expressions and estimate the emotions. The suggestion unit can also analyze the user's voice using voice analysis technology to estimate the emotions. For example, it analyzes the tone and speed of the voice to estimate the emotions. Furthermore, the suggestion unit can estimate the user's emotions based on survey results. For example, it analyzes the content of the survey answered by the user and estimates the emotions. Next, the suggestion unit adjusts the way suggestions are presented based on the estimated user's emotions. For example, if the user is nervous, it can provide simple, highly visible suggestions. If the user is relaxed, it can provide detailed suggestions. Furthermore, if the user is in a hurry, it can provide suggestions that focus on the main points. In this way, by adjusting the way suggestions are presented based on the user's emotions, it is possible to provide suggestions that are easy for the user to understand. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the suggestion unit may be performed using AI, or may be performed without using AI. For example, the suggestion unit may input user emotion data into the generation AI and cause the generation AI to adjust the way the proposal is expressed.

[0105] The proposal unit can adjust the level of detail of the proposal based on the importance of the solution when making a proposal. The proposal unit, for example, adjusts the level of detail of the proposal based on the importance of the solution. For example, a detailed proposal is made for a solution with high importance. Also, a simplified proposal can be made for a solution with low importance. Furthermore, the proposal unit can adjust the depth of the proposal according to the importance of the solution. For example, a detailed proposal is made for a solution with high importance, and a simplified proposal is made for a solution with low importance. In this way, by adjusting the level of detail of the proposal according to the importance of the solution, efficient proposals can be made. Some or all of the above-mentioned processing in the proposal unit may be performed using, or without, a generation AI. For example, the proposal unit can input solution importance data into the generation AI and cause the generation AI to adjust the level of detail of the proposal.

[0106] When making a proposal, the proposal unit can apply different proposal algorithms depending on the category of the solution. For example, the proposal unit applies different proposal algorithms depending on the category of the solution. For example, a process mining algorithm can be applied to solutions related to business flows. A statistical analysis algorithm can also be applied to solutions related to past business performance. A text mining algorithm can also be applied to solutions related to problems. For example, a process mining algorithm can be applied to business flow data, and a statistical analysis algorithm can be applied to past business performance data. This improves the accuracy of the proposal by applying an appropriate proposal algorithm depending on the category of the solution. Some or all of the above-mentioned processing in the proposal unit may be performed using, or without, a generation AI. For example, the proposal unit can input solution category data into the generation AI and cause the generation AI to apply the proposal algorithm.

[0107] When making a proposal, the proposal unit can improve the accuracy of the proposal by referring to past proposal results of each department. The proposal unit, for example, refers to past proposal results of each department. For example, the proposal algorithm is optimized based on past proposal reports and evaluation results. The proposal unit can also improve the accuracy of the proposal by referring to past proposal results of each department. Furthermore, the proposal unit can analyze past proposal results of each department and identify areas for improvement in the proposal. For example, the proposal unit improves the accuracy of the proposal based on past proposal results. In this way, the accuracy of the proposal is improved by referring to past proposal results. Some or all of the above-mentioned processing in the proposal unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the proposal unit can input past proposal result data into the generation AI and have the generation AI improve the accuracy of the proposal.

[0108] The suggestion unit can estimate the user's emotion and adjust the length of the suggestion based on the estimated user's emotion. The suggestion unit, for example, estimates the user's emotion. For example, it uses facial expression recognition technology to analyze the user's facial expression and estimate the emotion. The suggestion unit can also analyze the user's voice using voice analysis technology to estimate the emotion. For example, it analyzes the tone and speed of the voice and estimates the emotion. Furthermore, the suggestion unit can estimate the user's emotion based on survey results. For example, it analyzes the content of the survey answered by the user and estimates the emotion. Next, the suggestion unit adjusts the length of the suggestion based on the estimated user's emotion. For example, if the user is in a hurry, it can provide a short and to-the-point suggestion. Also, if the user is relaxed, it can provide a detailed suggestion. Furthermore, if the user is excited, it can provide a visually stimulating suggestion. In this way, by adjusting the length of the suggestion according to the user's emotion, it is possible to provide the optimal suggestion for the user. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the suggestion unit may be performed using AI, or may be performed without using AI. For example, the suggestion unit may input user emotion data to the generation AI and cause the generation AI to adjust the length of the suggestion.

[0109] When making a proposal, the proposal unit can determine the priority of the proposals based on the submission time of the solutions. The proposal unit, for example, determines the priority of the proposals based on the submission time of the solutions. For example, the most recent solutions are proposed preferentially. Also, solutions that were submitted earlier can be proposed later. Furthermore, the proposal unit can adjust the order of proposals based on the submission time. For example, the order of proposals is adjusted based on the submission time. In this way, by determining the priority of proposals based on the submission time of the solutions, the most recent solutions can be proposed preferentially. Some or all of the above-mentioned processing in the proposal unit may be performed using, or without, the generation AI. For example, the proposal unit can input solution submission time data into the generation AI and have the generation AI determine the priority of the proposals.

[0110] The suggestion unit can adjust the order of proposals based on the relevance of the solutions when proposing them. The suggestion unit, for example, adjusts the order of proposals based on the relevance of the solutions. For example, highly relevant solutions are preferentially proposed. Also, solutions with low relevance can be proposed later. Furthermore, the suggestion unit can adjust the order of proposals based on the relevance of the solutions. For example, the suggestion unit adjusts the order of proposals based on the relevance of the solutions. In this way, by adjusting the order of proposals based on the relevance of the solutions, highly relevant solutions can be preferentially proposed. Some or all of the above-described processing in the suggestion unit may be performed using, or without, the generation AI. For example, the suggestion unit can input relevance data of the solutions into the generation AI and cause the generation AI to adjust the order of proposals.

[0111] When making a proposal, the suggestion unit can adjust the use of technical terminology in the proposal according to the expertise level of each department. The suggestion unit, for example, adjusts the use of technical terminology in the proposal according to the expertise level of each department. For example, the suggestion unit can provide proposals that use a lot of technical terminology to departments with high expertise. It can also provide concise and easy-to-understand proposals to departments with low expertise. Furthermore, the suggestion unit can adjust the way the proposal is expressed according to the expertise level of each department. For example, the suggestion unit can provide proposals that use a lot of technical terminology to departments with high expertise, and provide concise and easy-to-understand proposals to departments with low expertise. By adjusting the use of technical terminology in the proposal according to the expertise level of each department, it is possible to provide easy-to-understand proposals. Some or all of the above-described processing in the suggestion unit may be performed using, or without, a generation AI. For example, the suggestion unit can input expertise level data of each department into the generation AI and cause the generation AI to execute the use of technical terminology in the proposal.

[0112] The generation unit can estimate the user's emotion and adjust the expression method of the product based on the estimated user's emotion. The generation unit, for example, estimates the user's emotion. For example, it uses facial expression recognition technology to analyze the user's facial expression and estimate the emotion. The generation unit can also analyze the user's voice using voice analysis technology to estimate the emotion. For example, it analyzes the tone and speed of the voice and estimates the emotion. Furthermore, the generation unit can estimate the user's emotion based on the results of a survey. For example, it analyzes the content of the survey answered by the user and estimates the emotion. Next, the generation unit adjusts the expression method of the product based on the estimated user's emotion. For example, if the user is nervous, a simple, highly visible product can be provided. Alternatively, if the user is relaxed, a detailed product can be provided. Furthermore, if the user is in a hurry, a product that focuses on the main points can be provided. By adjusting the expression method of the product according to the user's emotion, it is possible to provide a product that is easy for the user to understand. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit may input user emotion data into the generation AI and cause the generation AI to adjust the expression method of the product.

[0113] The generation unit can adjust the level of detail of the generation based on the importance of the product during generation. The generation unit adjusts the level of detail of the generation based on, for example, the importance of the product. For example, detailed generation is performed for products with high importance. Also, simplified generation can be performed for products with low importance. Furthermore, the generation unit can adjust the depth of generation according to the importance of the product. For example, detailed generation is performed for products with high importance, and simplified generation is performed for products with low importance. In this way, efficient generation is possible by adjusting the level of detail of generation according to the importance of the product. Some or all of the above-mentioned processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input product importance data to the generation AI and cause the generation AI to adjust the level of detail of generation.

[0114] The generation unit can apply different generation algorithms depending on the category of the product during generation. The generation unit applies different generation algorithms depending on, for example, the category of the product. For example, a text generation algorithm can be applied to a business procedure manual. A code generation algorithm can also be applied to an automation script. A slide generation algorithm can also be applied to presentation materials. For example, a text generation algorithm can be applied to a business procedure manual, and a code generation algorithm can be applied to an automation script. This improves the accuracy of generation by applying an appropriate generation algorithm depending on the category of the product. Some or all of the above-mentioned processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input product category data into the generation AI and cause the generation AI to apply the generation algorithm.

[0115] During generation, the generation unit can improve the accuracy of generation by referring to past generation results of each department. The generation unit, for example, refers to past generation results of each department. For example, the generation algorithm is optimized based on past generation reports and evaluation results. The generation unit can also improve the accuracy of generation by referring to past generation results of each department. Furthermore, the generation unit can analyze past generation results of each department and identify areas for improvement in generation. For example, the generation unit improves the accuracy of generation based on past generation results. In this way, the accuracy of generation is improved by referring to past generation results. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input past generation result data into the generation AI and cause the generation AI to improve the accuracy of generation.

[0116] The generation unit can estimate the user's emotion and adjust the length of the product based on the estimated user's emotion. The generation unit, for example, estimates the user's emotion. For example, it uses facial expression recognition technology to analyze the user's facial expression and estimate the emotion. The generation unit can also analyze the user's voice using voice analysis technology to estimate the emotion. For example, it analyzes the tone and speed of the voice and estimates the emotion. Furthermore, the generation unit can estimate the user's emotion based on the results of a survey. For example, it analyzes the content of the survey answered by the user and estimates the emotion. Next, the generation unit adjusts the length of the product based on the estimated user's emotion. For example, if the user is in a hurry, it can provide a short and to-the-point product. Also, if the user is relaxed, it can provide a detailed product. Furthermore, if the user is excited, it can provide a visually stimulating product. In this way, by adjusting the length of the product according to the user's emotion, it is possible to provide the optimal product for the user. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit may be performed using AI, or may be performed without using AI. For example, the generation unit may input user emotion data into the generation AI and cause the generation AI to adjust the length of the product.

[0117] At the time of generation, the generation unit can determine the generation priority based on the submission time of the product. The generation unit determines the generation priority based on, for example, the submission time of the product. For example, the newest product is generated preferentially. Also, products submitted earlier can be generated later. Furthermore, the generation unit can adjust the order of generation based on the submission time. For example, the order of generation is adjusted based on the submission time. In this way, by determining the generation priority based on the submission time of the product, the newest product can be generated preferentially. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input product submission time data into the generation AI and cause the generation AI to determine the generation priority.

[0118] The generation unit can adjust the order of generation based on the relevance of the products during generation. The generation unit adjusts the order of generation based on, for example, the relevance of the products. For example, highly relevant products are generated preferentially. Also, products with low relevance can be generated later. Furthermore, the generation unit can adjust the order of generation based on the relevance of the products. For example, the generation order is adjusted based on the relevance of the products. In this way, by adjusting the order of generation based on the relevance of the products, highly relevant products can be generated preferentially. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit inputs product relevance data into the generation AI and causes the generation AI to adjust the order of generation.

[0119] During generation, the generation unit can adjust the use of technical terminology in the product according to the expertise level of each department. The generation unit, for example, adjusts the use of technical terminology in the product according to the expertise level of each department. For example, it can provide a product that uses a lot of technical terminology to departments with high expertise. It can also provide a concise and easy-to-understand product to departments with low expertise. Furthermore, the generation unit can adjust the expression method of the product according to the expertise level of each department. For example, it can provide a product that uses a lot of technical terminology to departments with high expertise, and a concise and easy-to-understand product to departments with low expertise. In this way, by adjusting the use of technical terminology in the product according to the expertise level of each department, it is possible to provide a product that is easy to understand. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input expertise level data of each department into the generation AI and cause the generation AI to execute the use of technical terminology in the product.

[0120] The storage unit can estimate the user's emotions and determine the priority of products to be stored based on the estimated user's emotions. The storage unit, for example, estimates the user's emotions. For example, it uses facial expression recognition technology to analyze the user's facial expressions and estimate the emotions. The storage unit can also analyze the user's voice using voice analysis technology to estimate the emotions. For example, it analyzes the tone and speed of the voice to estimate the emotions. Furthermore, the storage unit can estimate the user's emotions based on survey results. For example, it analyzes the content of the survey answered by the user and estimates the emotions. Next, the storage unit determines the priority of products to be stored based on the estimated user's emotions. For example, if the user is stressed, it can prioritize storing products with high importance. Also, if the user is relaxed, it can prioritize storing detailed products. Furthermore, if the user is in a hurry, it can prioritize storing products that can be stored quickly. In this way, by determining the priority of products to be stored according to the user's emotions, important products can be prioritized. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the storage unit may be performed using AI, or may be performed without using AI. For example, the storage unit may input user emotion data into the generation AI and cause the generation AI to determine the priority of the products to be stored.

[0121] The storage unit can adjust the level of storage detail based on the importance of the product when storing. The storage unit adjusts the level of storage detail based on, for example, the importance of the product. For example, detailed storage is performed for products with high importance. Also, simplified storage can be performed for products with low importance. Furthermore, the storage unit can adjust the depth of storage according to the importance of the product. For example, detailed storage is performed for products with high importance, and simplified storage is performed for products with low importance. In this way, efficient storage is possible by adjusting the level of storage detail according to the importance of the product. Some or all of the above-mentioned processing in the storage unit may be performed using, for example, AI, or may be performed without using AI. For example, the storage unit can input product importance data to the generation AI and cause the generation AI to adjust the level of storage detail.

[0122] The storage unit can apply different storage algorithms depending on the category of the product when storing. The storage unit applies different storage algorithms depending on, for example, the category of the product. For example, a text storage algorithm can be applied to a business procedure manual. A code storage algorithm can also be applied to an automation script. A slide storage algorithm can also be applied to presentation materials. For example, a text storage algorithm can be applied to a business procedure manual, and a code storage algorithm can be applied to an automation script. This improves the accuracy of storage by applying an appropriate storage algorithm depending on the category of the product. Some or all of the above-mentioned processing in the storage unit may be performed using, for example, AI, or may be performed without using AI. For example, the storage unit can input product category data to a generation AI and cause the generation AI to apply a storage algorithm.

[0123] The storage unit can improve the accuracy of storage by referring to the past storage results of each department when storing data. The storage unit, for example, refers to the past storage results of each department. For example, the storage algorithm is optimized based on past storage reports and evaluation results. The storage unit can also improve the accuracy of storage by referring to the past storage results of each department. Furthermore, the storage unit can analyze the past storage results of each department and identify areas for improvement in storage. For example, the storage unit improves the accuracy of storage based on the past storage results. In this way, the accuracy of storage is improved by referring to the past storage results. Some or all of the above-mentioned processing in the storage unit may be performed using, for example, AI, or may be performed without using AI. For example, the storage unit can input past storage result data into a generation AI and have the generation AI improve the accuracy of storage.

[0124] The storage unit can estimate the user's emotions and adjust the display method of the stored product based on the estimated user's emotions. The storage unit, for example, estimates the user's emotions. For example, it uses facial expression recognition technology to analyze the user's facial expressions and estimate the emotions. The storage unit can also analyze the user's voice using voice analysis technology to estimate the emotions. For example, it analyzes the tone and speed of the voice to estimate the emotions. Furthermore, the storage unit can estimate the user's emotions based on survey results. For example, it analyzes the content of the survey answered by the user and estimates the emotions. Next, the storage unit adjusts the display method of the stored product based on the estimated user's emotions. For example, if the user is nervous, it can provide a simple, highly visible display method. If the user is relaxed, it can provide a detailed display method. Furthermore, if the user is in a hurry, it can provide a display method that focuses on the main points. In this way, by adjusting the display method of the stored product according to the user's emotions, it is possible to provide a display method that is easy for the user to understand. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the storage unit may be performed using AI, or may be performed without using AI. For example, the storage unit may input user emotion data into the generation AI and cause the generation AI to adjust the display method of the stored product.

[0125] The storage unit can determine the storage priority based on the submission time of the product when storing. The storage unit determines the storage priority based on, for example, the submission time of the product. For example, the most recent product is stored preferentially. Also, products submitted earlier can be stored later. Furthermore, the storage unit can adjust the storage order based on the submission time. For example, the storage order is adjusted based on the submission time. In this way, by determining the storage priority based on the submission time of the product, the most recent product can be stored preferentially. Some or all of the above-mentioned processing in the storage unit may be performed using, for example, AI, or may be performed without using AI. For example, the storage unit can input product submission time data to the generation AI and have the generation AI determine the storage priority.

[0126] The storage unit can adjust the storage order based on the relevance of the products when storing them. The storage unit adjusts the storage order based on, for example, the relevance of the products. For example, highly relevant products are stored preferentially. Also, products with low relevance can be stored later. Furthermore, the storage unit can adjust the storage order based on the relevance of the products. For example, the storage order is adjusted based on the relevance of the products. In this way, by adjusting the storage order based on the relevance of the products, highly relevant products can be stored preferentially. Some or all of the above-mentioned processing in the storage unit may be performed using, for example, AI, or may be performed without using AI. For example, the storage unit can input product relevance data to a generation AI and cause the generation AI to adjust the storage order.

[0127] The storage unit can adjust the use of technical terms in the stored content according to the expertise level of each department when storing the content. The storage unit, for example, adjusts the use of technical terms in the stored content according to the expertise level of each department. For example, it can provide stored content that uses a lot of technical terms to departments with high expertise. It can also provide concise and easy-to-understand stored content to departments with low expertise. Furthermore, the storage unit can adjust the way the stored content is expressed according to the expertise level of each department. For example, it can provide stored content that uses a lot of technical terms to departments with high expertise, and provide concise and easy-to-understand stored content to departments with low expertise. In this way, by adjusting the use of technical terms in the stored content according to the expertise level of each department, it is possible to provide easy-to-understand stored content. Some or all of the above-mentioned processing in the storage unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the storage unit can input expertise level data of each department into a generation AI and have the generation AI execute the use of technical terms in the stored content.

[0128] The reuse unit can estimate the user's emotions and prioritize the products to be reused based on the estimated user's emotions. The reuse unit, for example, estimates the user's emotions. For example, it uses facial expression recognition technology to analyze the user's facial expressions and estimate the emotions. The reuse unit can also analyze the user's voice using voice analysis technology to estimate the emotions. For example, it analyzes the tone and speed of the voice and estimates the emotions. The reuse unit can also estimate the user's emotions based on survey results. For example, it analyzes the content of the survey answered by the user and estimates the emotions. Next, the reuse unit prioritizes the products to be reused based on the estimated user's emotions. For example, if the user is stressed, it can prioritize the reuse of products with high importance. Also, if the user is relaxed, it can prioritize the reuse of detailed products. Furthermore, if the user is in a hurry, it can prioritize the reuse of products that can be reused quickly. In this way, by prioritizing the products to be reused according to the user's emotions, it is possible to prioritize the reuse of important products. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reuse unit may be performed using AI, or may be performed without using AI. For example, the reuse unit may input user emotion data into the generation AI and cause the generation AI to determine the priority of products to be reused.

[0129] The reuse unit can adjust the level of detail of reuse based on the importance of the product during reuse. The reuse unit adjusts the level of detail of reuse based on, for example, the importance of the product. For example, detailed reuse is performed for products with high importance. Also, simplified reuse can be performed for products with low importance. Furthermore, the reuse unit can adjust the depth of reuse according to the importance of the product. For example, detailed reuse is performed for products with high importance, and simplified reuse is performed for products with low importance. In this way, efficient reuse is possible by adjusting the level of detail of reuse according to the importance of the product. Some or all of the above-mentioned processing in the reuse unit may be performed using, for example, AI, or may be performed without using AI. For example, the reuse unit can input product importance data to the generation AI and cause the generation AI to adjust the level of detail of reuse.

[0130] The reuse unit can apply different reuse algorithms depending on the category of the product during reuse. For example, the reuse unit applies different reuse algorithms depending on the category of the product. For example, a text reuse algorithm can be applied to a business procedure manual. A code reuse algorithm can also be applied to an automation script. A slide reuse algorithm can also be applied to presentation materials. For example, a text reuse algorithm can be applied to a business procedure manual, and a code reuse algorithm can be applied to an automation script. This improves the accuracy of reuse by applying an appropriate reuse algorithm depending on the category of the product. Some or all of the above-mentioned processing in the reuse unit may be performed using, or without, a generation AI. For example, the reuse unit can input product category data into the generation AI and cause the generation AI to apply the reuse algorithm.

[0131] During reuse, the reuse unit can improve the accuracy of reuse by referring to the past reuse results of each department. The reuse unit, for example, refers to the past reuse results of each department. For example, the reuse algorithm is optimized based on past reuse reports and evaluation results. The reuse unit can also improve the accuracy of reuse by referring to the past reuse results of each department. Furthermore, the reuse unit can analyze the past reuse results of each department and identify areas for improvement in reuse. For example, the reuse unit improves the accuracy of reuse based on the past reuse results. In this way, the accuracy of reuse is improved by referring to the past reuse results. Some or all of the above-mentioned processing in the reuse unit may be performed, for example, using AI, or may be performed without using AI. For example, the reuse unit can input past reuse result data into the generation AI and have the generation AI improve the accuracy of reuse.

[0132] The reuse unit can estimate the user's emotions and adjust the display method of the reused product based on the estimated user's emotions. The reuse unit, for example, estimates the user's emotions. For example, it uses facial expression recognition technology to analyze the user's facial expressions and estimate the emotions. The reuse unit can also analyze the user's voice using voice analysis technology to estimate the emotions. For example, it analyzes the tone and speed of the voice and estimates the emotions. The reuse unit can also estimate the user's emotions based on survey results. For example, it analyzes the content of the survey answered by the user and estimates the emotions. Next, the reuse unit adjusts the display method of the reused product based on the estimated user's emotions. For example, if the user is nervous, it can provide a simple, highly visible display method. If the user is relaxed, it can provide a detailed display method. Furthermore, if the user is in a hurry, it can provide a display method that focuses on the main points. In this way, by adjusting the display method of the reused product according to the user's emotions, it is possible to provide a display method that is easy for the user to understand. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reuse unit may be performed using AI, or may be performed without using AI. For example, the reuse unit may input user emotion data into the generation AI and cause the generation AI to adjust the display method of the product to be reused.

[0133] At the time of reuse, the reuse unit can determine the priority of reuse based on the submission time of the product. The reuse unit determines the priority of reuse based on, for example, the submission time of the product. For example, the most recent product is reused preferentially. Also, products submitted earlier can be reused later. Furthermore, the reuse unit can adjust the order of reuse based on the submission time. For example, the order of reuse is adjusted based on the submission time. In this way, by determining the priority of reuse based on the submission time of the product, the most recent product can be reused preferentially. Some or all of the above-mentioned processing in the reuse unit may be performed using, for example, AI, or may be performed without using AI. For example, the reuse unit can input product submission time data into the generation AI and cause the generation AI to determine the priority of reuse.

[0134] The reuse unit can adjust the order of reuse based on the relevance of the products during reuse. The reuse unit adjusts the order of reuse based on, for example, the relevance of the products. For example, highly relevant products are reused preferentially. Also, products with low relevance can be reused later. Furthermore, the reuse unit can also adjust the order of reuse based on the relevance of the products. For example, the order of reuse is adjusted based on the relevance of the products. In this way, by adjusting the order of reuse based on the relevance of the products, highly relevant products can be reused preferentially. Some or all of the above-mentioned processing in the reuse unit may be performed using, for example, AI, or may be performed without using AI. For example, the reuse unit inputs product relevance data to the generation AI and causes the generation AI to adjust the order of reuse.

[0135] During reuse, the reuse unit can adjust the use of technical terminology in the reused material according to the expertise level of each department. The reuse unit, for example, adjusts the use of technical terminology in the reused material according to the expertise level of each department. For example, it can provide reused material that uses a lot of technical terminology to departments with high expertise. It can also provide reused material that is concise and easy to understand to departments with low expertise. Furthermore, the reuse unit can adjust the way the reused material is expressed according to the expertise level of each department. For example, it can provide reused material that uses a lot of technical terminology to departments with high expertise, and provide reused material that is concise and easy to understand to departments with low expertise. By adjusting the use of technical terminology in the reused material according to the expertise level of each department, it is possible to provide reused material that is easy to understand. Some or all of the above-described processing in the reuse unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the reuse unit can input expertise level data of each department into the generation AI and have the generation AI execute the use of technical terminology in the reused material. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, proposal unit, generation unit, storage unit, and reuse unit, is implemented, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects business-related information from each department within the company using the camera 42 and microphone 38B of the smart device 14. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected information. The proposal unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and proposes practical solutions based on the analysis results. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and creates a product based on the proposed solution. The storage unit is implemented, for example, by the control unit 46A of the smart device 14 and stores the product in a shared folder within the company. The reuse unit is implemented, for example, by the control unit 46A of the smart device 14 and reuses the stored product. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, proposal unit, generation unit, storage unit, and reuse unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects business-related information from each department within the company using the camera 42 and microphone 238 of the smart glasses 214. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected information. The proposal unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and proposes practical solutions based on the analysis results. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and creates a product based on the proposed solution. The storage unit is realized, for example, by the control unit 46A of the smart glasses 214 and stores the product in a shared folder within the company. The reuse unit is realized, for example, by the control unit 46A of the smart glasses 214 and reuses the stored product. === Hard Collateral 1-3 === Each of the multiple elements, including the collection unit, analysis unit, proposal unit, generation unit, storage unit, and reuse unit, is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the collection unit collects business-related information from each department within the company using the camera 42 and microphone 238 of the headset type terminal 314. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected information. The proposal unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and proposes practical solutions based on the analysis results. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and creates a product based on the proposed solution. The storage unit is realized, for example, by the control unit 46A of the headset type terminal 314 and stores the product in a shared folder within the company. The reuse unit is realized, for example, by the control unit 46A of the headset type terminal 314 and reuses the stored product. === Hard Collateral 1-4 === Each of the multiple elements, including the collection unit, analysis unit, proposal unit, generation unit, storage unit, and reuse unit, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects business-related information from each department within the company using the camera 42 and microphone 238 of the robot 414. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected information. The proposal unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and proposes practical solutions based on the analysis results. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and creates a product based on the proposed solution. The storage unit is realized, for example, by the control unit 46A of the robot 414 and stores the product in a shared folder within the company. The reuse unit is realized, for example, by the control unit 46A of the robot 414 and reuses the stored product.

[0136] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0137] The analysis unit can also estimate the user's emotions and determine analysis priorities based on the estimated user emotions. For example, if the user is feeling stressed, it can prioritize analysis of important information and provide results quickly. If the user is relaxed, it can perform detailed analysis and provide comprehensive results. Furthermore, if the user is in a hurry, it can provide concise analysis results that focus on the main points. By adjusting the analysis priorities according to the user's emotions, it is possible to provide optimal analysis results for the user. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's emotion data into a generation AI and have the generation AI determine the analysis priorities.

[0138] When collecting information on each department's work flow, past work performance, and problems, the collection unit can also adjust the timing of information collection taking into account each department's busy and slow periods. For example, the collection unit can reduce the frequency of information collection during busy periods and collect detailed information during slow periods. The timing of information collection can also be adjusted depending on the progress of a specific project. This reduces the workload and enables efficient information collection. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input work schedule data for each department into the generation AI and have the generation AI adjust the timing of information collection.

[0139] The suggestion unit can also estimate the user's emotions and adjust the content of the suggestions based on the estimated user emotions. For example, if the user is feeling stressed, the suggestion unit can provide concise and easy-to-implement suggestions. If the user is relaxed, the suggestion unit can provide detailed suggestions and present multiple options. Furthermore, if the user is in a hurry, the suggestion unit can provide quick suggestions that focus on the main points. This allows the suggestion unit to adjust the content of the suggestions according to the user's emotions, thereby providing the most suitable suggestions for the user. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the user's emotion data into a generation AI and have the generation AI adjust the content of the suggestions.

[0140] When creating a product, the generation unit can also adjust the format of the product according to the characteristics of each department's work. For example, it can provide detailed technical specifications to the engineering department and simple presentation materials to the sales department. It can also customize the content of the product according to each department's work flow. This can improve work efficiency by providing products that meet the needs of each department. Some or all of the above-mentioned processing in the generation unit can be performed using, for example, AI, or can be performed without using AI. For example, the generation unit can input business data from each department into the generation AI and have the generation AI adjust the format of the product.

[0141] When storing a product, the storage unit can estimate the user's emotions and adjust the display method of the stored product based on the estimated user emotions. For example, if the user is nervous, a simple, highly visible display method can be provided. If the user is relaxed, a detailed display method can be provided. Furthermore, if the user is in a hurry, a display method that focuses on the main points can be provided. By adjusting the display method of the stored product according to the user's emotions, a display method that is easy for the user to understand can be provided. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. Some or all of the above-mentioned processing in the storage unit may be performed using, for example, AI, or may be performed without using AI. For example, the storage unit can input the user's emotion data into the generation AI and have the generation AI adjust the display method of the stored product.

[0142] When selecting artifacts to reuse, the reuse department can select the most appropriate artifacts by taking into consideration the characteristics of each department's work and the progress of the current project. For example, the reuse department can provide artifacts containing technical details to the engineering department and presentation materials to the sales department. It can also provide artifacts related to ongoing projects preferentially. This allows for the reuse of artifacts that meet the needs of each department, thereby improving work efficiency. Some or all of the above-mentioned processing in the reuse department can be performed using, or without, AI. For example, the reuse department can input business data and project data from each department into a generation AI and have the generation AI select the most appropriate artifacts.

[0143] The collection unit can also estimate the user's emotions and prioritize the information to be collected based on the estimated user emotions. For example, if the user is feeling stressed, it can prioritize collecting important information. Also, if the user is relaxed, it can prioritize collecting detailed information. Furthermore, if the user is in a hurry, it can prioritize collecting information that can be collected quickly. Thus, by prioritizing information according to the user's emotions, important information can be collected preferentially. The emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or without AI. For example, the collection unit can input the user's emotion data into a generation AI and have the generation AI determine the priority of the information.

[0144] When providing analysis results, the analysis unit can provide optimal analysis results by taking into account the characteristics of each department's work and the progress of current projects. For example, analysis results including technical details can be provided to the engineering department, and concise analysis results can be provided to the sales department. Analysis results related to ongoing projects can also be provided preferentially. This allows for the provision of analysis results tailored to the needs of each department, thereby improving work efficiency. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input business data and project data from each department into the generation AI and have the generation AI provide optimal analysis results.

[0145] The suggestion unit can also estimate the user's emotions and adjust the way suggestions are expressed based on the estimated user emotions. For example, if the user is nervous, a simple, highly visible suggestion can be provided. If the user is relaxed, a detailed suggestion can be provided. Furthermore, if the user is in a hurry, a suggestion that focuses on the main points can be provided. By adjusting the way suggestions are expressed according to the user's emotions, suggestions that are easy for the user to understand can be provided. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the user's emotion data into a generation AI and have the generation AI adjust the way suggestions are expressed.

[0146] When creating a product, the generation unit can also improve the accuracy of generation by referring to past generation results from each department. For example, the generation algorithm can be optimized based on past generation reports and evaluation results. The generation unit can also analyze past generation results from each department to identify areas for improvement in generation. In this way, by referring to past generation results, the accuracy of generation can be improved. Some or all of the above-mentioned processing in the generation unit can be performed using, for example, AI, or can be performed without using AI. For example, the generation unit can input past generation result data into the generation AI and have the generation AI improve the accuracy of generation.

[0147] The processing flow of the second embodiment will be briefly explained below.

[0148] Step 1: The collection department collects information about operations from each department within the company. These departments include the sales department, development department, and human resources department. The collection department collects information about each department's business flow, past business performance, problems, etc. Methods of collection include questionnaires, interviews, and extraction from databases. Step 2: The analysis unit uses the generation AI to analyze the information collected by the collection unit. Analysis methods include data mining, statistical analysis, and machine learning algorithms. The analysis unit identifies bottlenecks in specific business processes based on the collected information and proposes efficient business procedures based on past business performance. Step 3: The Proposal Department uses generative AI to propose practical solutions based on the analysis results obtained by the Analysis Department. Suggestions include improving business procedures, introducing tools, and automating processes. The Proposal Department proposes efficient business procedures based on the analysis results. Step 4: The generation department uses the generation AI to create products based on the solutions proposed by the proposal department. Products include work procedures, automation scripts, reports, etc. The generation department creates work procedures, including detailed procedures, diagrams, checklists, etc. It also creates automation scripts, clarifying the programming language used and the script's functions. Step 5: The storage unit stores the product created by the generation unit in a shared folder within the company. The shared folder can be a specific directory on an internal server or cloud storage. The storage unit saves the product in a database and uploads it to the shared folder. Step 6: The reuse department reuses the artifacts stored in the repository. Reuse methods include using them in other projects or applying them to other departments. The reuse department downloads the artifacts from the shared folder and applies them to their own work.

[0149] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

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

[0151] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

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

[0153] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0154] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

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

[0156] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

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

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

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

[0160] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0161] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0162] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0163] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0164] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0165] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0166] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0167] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0168] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0169] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0170] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0171] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0172] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0173] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0174] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0175] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0176] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0177] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0178] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0179] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0180] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0181] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0182] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0183] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0184] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0185] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0186] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0187] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0188] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0189] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0190] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0191] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0192] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0193] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0194] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0195] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0196] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0197] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0198] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0199] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0200] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0201] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0202] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0203] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0204] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0205] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0206] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0207] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0208] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0209] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0210] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0211] 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.

[0212] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0213] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0214] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0215] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0216] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0217] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0218] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0219] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0220] [Explanation of symbols]

[0221] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. The collection department collects information about business operations from each department within the company, an analysis unit that analyzes the information collected by the collection unit; a proposal unit that proposes practical solutions based on the analysis results obtained by the analysis unit; a generation unit that generates a product based on the solution proposed by the proposal unit; a storage unit for storing the product created by the generation unit; a recycling unit that reuses the product stored in the storage unit A system characterized by:

2. The collecting unit Collect information including the business flow of each department, past business performance, and problems 2. The system of claim 1.

3. The analysis unit Analyze the collected information and identify bottlenecks in specific business processes 2. The system of claim 1.

4. The proposal unit Proposing efficient work procedures based on analysis results 2. The system of claim 1.

5. The generation unit Create artifacts including operating procedures and automation scripts 2. The system of claim 1.

6. The storage unit is Store the generated data in a shared folder within the company 2. The system of claim 1.

7. The recycling unit includes: Download the generated work from a shared folder and let other employees reuse it 2. The system of claim 1.

8. The collecting unit Estimate the user's emotions and adjust the timing of information collection based on the estimated user emotions.

2. The system of claim 1.

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