system

Abstract

The system according to the embodiment aims to enable end users to easily create RPA robots. [Solution] A system according to an embodiment includes a reception unit, a generation unit, and a creation unit. The reception unit receives business process input from an end user. The generation unit analyzes the business process input received by the reception unit and generates an operation scenario. The creation unit creates an RPA robot based on the operation scenario generated by the generation unit.

Description

[Technical Field]

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

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

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

[0004] With conventional technology, creating an RPA robot required a high level of skill and development time, which placed a heavy burden on end users.

[0005] The system according to the embodiment aims to enable end users to easily create RPA robots. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a generation unit, and a creation unit. The reception unit receives business process input from an end user. The generation unit analyzes the business process input received by the reception unit and generates an operation scenario. The creation unit creates an RPA robot based on the operation scenario generated by the generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can enable end users to easily create RPA robots. [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) In an embodiment of the present invention, an RPA robot creation support system allows end users to input the business process they want to automate in natural language. A generation AI analyzes the input and generates an appropriate RPA robot operation scenario, automatically creating the RPA robot. This system allows end users to easily create RPA robots without requiring specialized programming skills. For example, an end user inputs a business process in natural language, such as "Check email every day at 9:00 and move emails containing specific keywords to a folder." This information is input into the generation AI, which analyzes it and generates specific procedures. An RPA robot is automatically created based on the generated procedures, and end users can use the robot to automate their business processes. This allows end users to easily create RPA robots without requiring specialized programming skills. In addition, because the generation AI automatically generates operation scenarios, development time is significantly reduced. For example, even users without programming knowledge can create an automated RPA robot simply by inputting the business process in natural language. This allows end users to efficiently automate business processes. The RPA robot creation support system allows end users to create RPA robots and automate business processes without requiring specialized skills.

[0029] An RPA robot creation support system according to an embodiment includes a reception unit, a generation unit, and a creation unit. The reception unit receives input of a business process from an end user. The end user can input a business process in natural language, such as, for example, "Check email every day at 9:00 and move emails containing a specific keyword to a folder." The reception unit passes this input to a generation AI. The generation unit uses the generation AI to analyze the business process input received by the reception unit and generate an operation scenario. The generation AI analyzes the input business process using, for example, natural language processing technology and generates specific steps. For example, steps such as "check email," "search for a specific keyword," and "move corresponding emails to a folder" are generated. The generation unit passes the generated operation scenario to the creation unit. The creation unit creates an RPA robot based on the operation scenario generated by the generation unit. For example, the RPA robot checks email, searches for a specific keyword, and moves corresponding emails to a folder according to the generated steps. In this way, an end user can execute an automated business process. This allows the RPA robot creation support system according to an embodiment to create an RPA robot and automate business processes without requiring specialized skills from end users.

[0030] The reception unit allows an end user to input a business process they wish to automate in natural language. Natural languages ​​include, but are not limited to, Japanese, English, and other languages. For example, the reception unit allows an end user to input a business process in natural language, such as "check email every day at 9:00 and move emails containing specific keywords to a folder." This allows the end user to input a business process without requiring specialized programming skills. Some or all of the above-described processing in the reception unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the reception unit can input the business process in natural language input by the end user to a generation AI, which can then analyze the content.

[0031] The generation unit can use a generation AI to analyze the business process input received by the reception unit and generate an operation scenario. Examples of the generation AI include, but are not limited to, GPT-4 (registered trademark), Gemini, and other generative models. The generation unit can use, for example, a generation AI to analyze the business process input by the end user and generate an operation scenario. The generation AI can analyze the input business process using, for example, natural language processing technology and generate specific steps. For example, steps such as "check email," "search for a specific keyword," and "move the corresponding email to a folder" are generated. This makes it possible to analyze the business process and generate an operation scenario using the generation AI. Some or all of the above-mentioned processing in the generation unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the generation unit can input the business process input by the end user into the generation AI, and the generation AI can analyze the content and generate an operation scenario.

[0032] The generation unit can use a generation AI to generate specific procedures for automating a business process. Examples of specific procedures include, but are not limited to, step-by-step instructions, conditional branching, and error handling. The generation unit, for example, uses a generation AI to analyze a business process entered by an end user and generate specific procedures. The generation AI can analyze the entered business process using, for example, natural language processing technology and generate specific procedures. For example, procedures such as "check email," "search for a specific keyword," and "move the corresponding email to a folder" are generated. This enables the automation of business processes by generating specific procedures using the generation AI. Some or all of the above-described processing in the generation unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the generation unit can input a business process entered by an end user into a generation AI, which can then analyze the content and generate specific procedures.

[0033] The creation unit can create an RPA robot based on the operation scenario generated by the generation unit. The RPA robot may include, but is not limited to, the software used, the type of task to be performed, and the interface. For example, the creation unit creates the RPA robot based on the operation scenario generated by the generation unit. According to the generated procedure, the RPA robot checks emails, searches for specific keywords, and moves the corresponding emails to a folder. In this way, an end user can execute an automated business process. By creating an RPA robot based on the generated operation scenario, an end user can execute an automated business process. Some or all of the above-described processing in the creation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the creation unit may input the operation scenario generated by the generation unit into a generation AI, which then analyzes the contents of the scenario to create an RPA robot.

[0034] The reception unit can analyze the user's past business process input history and provide an optimal input interface. Examples of optimal input interfaces include, but are not limited to, GUI, voice input, and touch screen. For example, the reception unit can automatically display business processes that the user has frequently input in the past as candidates. The reception unit can also prioritize and suggest input methods (voice, text, etc.) that the user has used in the past. The reception unit can also predict and suggest business processes to be used in specific time periods based on the user's past input history. By analyzing the past input history, the reception unit can provide an optimal input interface for the user. Some or all of the above-described processing in the reception unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the reception unit can input the user's past input history data into a generation AI, which can then analyze the content and provide an optimal input interface.

[0035] When inputting a business process, the reception unit can filter the input content based on the user's current work situation and areas of interest. Examples of work situation include, but are not limited to, current tasks and project progress. Examples of areas of interest include, but are not limited to, specific business domains and technical fields. For example, the reception unit can prioritize displaying business processes related to a project currently being carried out by the user. The reception unit can also suggest related business processes based on the user's areas of interest. The reception unit can also filter and display optimal business processes taking into account the user's current work situation. This allows the user to input more relevant business processes by filtering the input content based on the user's work situation and areas of interest. Some or all of the above-described processing in the reception unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the reception unit can input data on the user's work situation and areas of interest into the generation AI, which can then analyze the data and filter the input content.

[0036] When inputting business processes, the reception unit can prioritize inputting highly relevant business processes by taking into account the user's geographical location information. Examples of geographical location information include, but are not limited to, GPS data, IP addresses, and location information services. For example, when the user is in a specific location, the reception unit can prioritize displaying business processes related to that location. The reception unit can also prioritize displaying business processes to be performed in locations close to the user's current location. The reception unit can also suggest optimal business processes based on the user's geographical location information. This allows highly relevant business processes to be input preferentially by taking the user's geographical location information into consideration. Some or all of the above-described processing in the reception unit can be performed using, or without, a generation AI. For example, the reception unit can input the user's geographical location information data into the generation AI, which can then analyze the data and input highly relevant business processes preferentially.

[0037] The reception unit can analyze the user's social media activity when inputting a business process and input the related business process. Social media activity includes, but is not limited to, for example, the content of posts, the number of likes, and the number of followers. The reception unit, for example, can prioritize and display business processes mentioned by the user on social media. The reception unit can also suggest related business processes based on the user's social media activity. The reception unit can also display optimal business processes based on the user's social media interests. This allows the related business process to be input by analyzing the social media activity. Some or all of the above-described processing in the reception unit can be performed, for example, using a generation AI or without using a generation AI. For example, the reception unit can input the user's social media activity data to the generation AI, which can then analyze the content and input the related business process.

[0038] When generating an operational scenario, the generation unit can adjust the level of detail of the scenario based on the importance of the business process. Examples of importance include, but are not limited to, the priority level, impact level, and urgency of the business. For example, the generation unit generates a detailed operational scenario for a business process with high importance. The generation unit can also generate a concise operational scenario for a business process with low importance. The generation unit can also dynamically adjust the level of detail of the scenario according to the importance of the business process. This allows for the generation of a more appropriate scenario by adjusting the level of detail of the scenario based on the importance of the business process. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input business process importance data into the generation AI, which can then analyze the data and adjust the level of detail of the scenario.

[0039] When generating an operational scenario, the generation unit can apply different generation algorithms depending on the category of the business process. Examples of categories include, but are not limited to, the type of business process and the phase of the project. For example, the generation unit can apply a specific algorithm to a data entry task to generate an operational scenario. The generation unit can also apply a different algorithm to a data analysis task to generate an operational scenario. The generation unit can also select an optimal generation algorithm depending on the category of the business process to generate an operational scenario. By applying the optimal generation algorithm depending on the category of the business process, a more appropriate scenario can be generated. Some or all of the above-described processing in the generation unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the generation unit can input business process category data into the generation AI, which can analyze the data and apply the optimal generation algorithm.

[0040] When generating operational scenarios, the generation unit can determine the priority of the scenarios based on the submission times of the business processes. Examples of submission times include, but are not limited to, submission deadlines and project schedules. For example, the generation unit can prioritize generating operational scenarios for business processes with upcoming submission deadlines. The generation unit can also postpone generating operational scenarios for business processes with more distant submission deadlines. The generation unit can also dynamically adjust the priority of scenarios based on the submission times of the business processes. By determining the priority of scenarios based on the submission times of the business processes, scenarios can be generated in a more appropriate order. 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 submission time data of business processes into the generation AI, which can then analyze the data and determine the priority of the scenarios.

[0041] When generating an operational scenario, the generation unit can adjust the order of the scenarios based on the relevance of the business processes. Examples of relevance include, but are not limited to, the degree of relevance of the business processes and project dependencies. For example, the generation unit prioritizes placing highly relevant business processes in the first half of the scenario. The generation unit can also place less relevant business processes in the second half of the scenario. The generation unit can also dynamically adjust the order of the scenarios based on the relevance of the business processes. By adjusting the order of the scenarios based on the relevance of the business processes, scenarios can be generated in a more appropriate order. 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 relevance data of the business processes into the generation AI, which can then analyze the data and adjust the order of the scenarios.

[0042] When creating an RPA robot, the creation unit can analyze the user's past business processes and select the optimal creation method. Past business processes include, but are not limited to, past task history and project deliverables. The creation unit can, for example, propose the optimal creation method based on data of RPA robots created by the user in the past. The creation unit can also analyze the user's past business processes and select an efficient creation method. The creation unit can also propose the optimal creation procedure based on the user's past business processes. In this way, the optimal creation method can be selected by analyzing the past business processes. Some or all of the above-mentioned processing in the creation unit can be performed, for example, using a generation AI or without using a generation AI. For example, the creation unit can input the user's past business process data into the generation AI, which can then analyze the content and select the optimal creation method.

[0043] When creating an RPA robot, the creation unit can customize the creation means based on the user's current work situation. The current work situation includes, but is not limited to, the user's current task and project progress. For example, the creation unit prioritizes the creation of an RPA robot related to a project currently underway by the user. The creation unit can also propose an optimal creation means taking into account the user's current work situation. The creation unit can also create a customized RPA robot based on the user's current work situation. This allows customizing the creation means based on the current work situation to create a more appropriate RPA robot. Some or all of the above-described processing in the creation unit may be performed using, or without, a generation AI. For example, the creation unit can input the user's current work situation data into the generation AI, which can then analyze the data and customize the creation means.

[0044] When creating an RPA robot, the creation unit can select the optimal creation method by taking into account the user's geographical location information. Examples of geographical location information include, but are not limited to, GPS data, IP addresses, and location information services. For example, if the user is in a specific location, the creation unit prioritizes creating an RPA robot related to that location. The creation unit can also create an RPA robot corresponding to a business process performed in a location close to the user's current location. The creation unit can also suggest the optimal creation method based on the user's geographical location information. This allows for the selection of a more appropriate creation method by taking the geographical location information into consideration. Some or all of the above-described processing in the creation unit may be performed using, or without, a generation AI. For example, the creation unit can input the user's geographical location information data into the generation AI, which can then analyze the data and select the optimal creation method.

[0045] When creating an RPA robot, the creation unit can analyze a user's social media activity and suggest a creation method. Social media activity includes, but is not limited to, post content, the number of likes, and the number of followers. For example, the creation unit creates an RPA robot corresponding to a business process mentioned by the user on social media. The creation unit can also suggest a creation method for a related RPA robot based on the user's social media activity. The creation unit can also suggest an optimal creation method based on the user's social media interests. In this way, analyzing social media activity can suggest a more appropriate creation method. Some or all of the above-described processing in the creation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the creation unit can input the user's social media activity data into the generation AI, which can then analyze the content and suggest a creation method.

[0046] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0047] The reception unit can analyze the user's past input history and automatically suggest frequently used business processes. For example, if the user has frequently input the business process "Create meeting minutes every Monday" in the past, the reception unit will automatically display that business process as a candidate. It can also prioritize suggestions based on input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest business processes that will be used during specific time periods based on the user's past input history. In this way, by analyzing the user's past input history, it is possible to provide the user with the optimal input interface.

[0048] When generating an operational scenario, the generation unit can adjust the level of detail of the scenario based on the importance of the business process. For example, a detailed operational scenario can be generated for a business process with high importance, and a simple operational scenario can be generated for a business process with low importance. The generation unit can also dynamically adjust the level of detail of the scenario according to the importance of the business process. This allows for the generation of a more appropriate scenario by adjusting the level of detail of the scenario based on the importance of the business process.

[0049] When generating an operational scenario, the generation unit can apply different generation algorithms depending on the business process category. For example, a specific algorithm can be applied to generate an operational scenario for a data entry task, and a different algorithm can be applied to generate an operational scenario for a data analysis task. The generation unit can also select the optimal generation algorithm depending on the business process category to generate an operational scenario. This makes it possible to generate a more appropriate scenario by applying the optimal generation algorithm depending on the business process category.

[0050] When creating an RPA robot, the creation unit can analyze the user's past business processes and select the optimal creation method. For example, the creation unit can suggest the optimal creation method based on data from RPA robots created by the user in the past. The creation unit can also analyze the user's past business processes and select an efficient creation method. Furthermore, the creation unit can suggest the optimal creation procedure based on the user's past business processes. This makes it possible to select the optimal creation method by analyzing past business processes.

[0051] When creating an RPA robot, the creation unit can customize the creation method based on the user's current work situation. For example, the creation unit can prioritize the creation of an RPA robot related to a project currently underway by the user. The creation unit can also propose the optimal creation method taking into account the user's current work situation. Furthermore, the creation unit can create a customized RPA robot based on the user's current work situation. This allows the creation of a more appropriate RPA robot by customizing the creation method based on the current work situation.

[0052] The processing flow of the first embodiment will be briefly explained below.

[0053] Step 1: The reception unit accepts business process input from the end user. For example, the end user can input a business process in natural language, such as "Check email every day at 9:00 and move emails containing specific keywords to a folder." The reception unit passes this input to the generation AI. Step 2: The generation unit uses a generation AI to analyze the business process input received by the reception unit and generate an operation scenario. The generation AI analyzes the input business process using, for example, natural language processing technology and generates specific steps. For example, steps such as "check email," "search for specific keywords," and "move relevant emails to a folder" are generated. The generation unit passes the generated operation scenario to the creation unit. Step 3: The creation unit creates an RPA robot based on the operation scenario generated by the generation unit. For example, the RPA robot follows the generated procedure to check emails, search for specific keywords, and move relevant emails to a folder.

[0054] (Example 2) In an embodiment of the present invention, an RPA robot creation support system allows end users to input the business process they want to automate in natural language. A generation AI analyzes the input and generates an appropriate RPA robot operation scenario, automatically creating the RPA robot. This system allows end users to easily create RPA robots without requiring specialized programming skills. For example, an end user inputs a business process in natural language, such as "Check email every day at 9:00 and move emails containing specific keywords to a folder." This information is input into the generation AI, which analyzes it and generates specific procedures. An RPA robot is automatically created based on the generated procedures, and end users can use the robot to automate their business processes. This allows end users to easily create RPA robots without requiring specialized programming skills. In addition, because the generation AI automatically generates operation scenarios, development time is significantly reduced. For example, even users without programming knowledge can create an automated RPA robot simply by inputting the business process in natural language. This allows end users to efficiently automate business processes. The RPA robot creation support system allows end users to create RPA robots and automate business processes without requiring specialized skills.

[0055] An RPA robot creation support system according to an embodiment includes a reception unit, a generation unit, and a creation unit. The reception unit receives input of a business process from an end user. The end user can input a business process in natural language, such as, for example, "Check email every day at 9:00 and move emails containing a specific keyword to a folder." The reception unit passes this input to a generation AI. The generation unit uses the generation AI to analyze the business process input received by the reception unit and generate an operation scenario. The generation AI analyzes the input business process using, for example, natural language processing technology and generates specific steps. For example, steps such as "check email," "search for a specific keyword," and "move corresponding emails to a folder" are generated. The generation unit passes the generated operation scenario to the creation unit. The creation unit creates an RPA robot based on the operation scenario generated by the generation unit. For example, the RPA robot checks email, searches for a specific keyword, and moves corresponding emails to a folder according to the generated steps. In this way, an end user can execute an automated business process. This allows the RPA robot creation support system according to an embodiment to create an RPA robot and automate business processes without requiring specialized skills from end users.

[0056] The reception unit allows an end user to input a business process they wish to automate in natural language. Natural languages ​​include, but are not limited to, Japanese, English, and other languages. For example, the reception unit allows an end user to input a business process in natural language, such as "check email every day at 9:00 and move emails containing specific keywords to a folder." This allows the end user to input a business process without requiring specialized programming skills. Some or all of the above-described processing in the reception unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the reception unit can input the business process in natural language input by the end user to a generation AI, which can then analyze the content.

[0057] The generation unit can use a generation AI to analyze the business process input received by the reception unit and generate an operational scenario. Examples of the generation AI include, but are not limited to, GPT-4, Gemini, and other generative models. The generation unit can use, for example, a generation AI to analyze the business process input by the end user and generate an operational scenario. The generation AI can analyze the input business process using, for example, natural language processing technology and generate specific steps. For example, steps such as "check email," "search for a specific keyword," and "move the corresponding email to a folder" are generated. This makes it possible to analyze the business process and generate an operational scenario using the generation AI. Some or all of the above-mentioned processing in the generation unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the generation unit can input the business process input by the end user into the generation AI, which can then analyze the content and generate an operational scenario.

[0058] The generation unit can use a generation AI to generate specific procedures for automating a business process. Examples of specific procedures include, but are not limited to, step-by-step instructions, conditional branching, and error handling. The generation unit, for example, uses a generation AI to analyze a business process entered by an end user and generate specific procedures. The generation AI can analyze the entered business process using, for example, natural language processing technology and generate specific procedures. For example, procedures such as "check email," "search for a specific keyword," and "move the corresponding email to a folder" are generated. This enables the automation of business processes by generating specific procedures using the generation AI. Some or all of the above-described processing in the generation unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the generation unit can input a business process entered by an end user into a generation AI, which can then analyze the content and generate specific procedures.

[0059] The creation unit can create an RPA robot based on the operation scenario generated by the generation unit. The RPA robot may include, but is not limited to, the software used, the type of task to be performed, and the interface. For example, the creation unit creates the RPA robot based on the operation scenario generated by the generation unit. According to the generated procedure, the RPA robot checks emails, searches for specific keywords, and moves the corresponding emails to a folder. In this way, an end user can execute an automated business process. By creating an RPA robot based on the generated operation scenario, an end user can execute an automated business process. Some or all of the above-described processing in the creation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the creation unit may input the operation scenario generated by the generation unit into a generation AI, which then analyzes the contents of the scenario to create an RPA robot.

[0060] The reception unit can estimate the user's emotions and adjust the input method for the business process based on the estimated user emotions. Emotions include, but are not limited to, joy, sadness, anger, etc. For example, when the user is stressed, the reception unit can provide a simple interface and minimize input steps. Furthermore, when the user is relaxed, the reception unit can provide detailed input options and suggest a customizable input method. Furthermore, when the user is in a hurry, the reception unit can prioritize voice input to enable quick input of the business process. This enables more appropriate input of the business process by adjusting the input method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the reception unit may be performed using, for example, the generation AI. For example, the reception unit can input the user's emotion data into the generation AI, which can then analyze the data and adjust the input method.

[0061] The reception unit can analyze the user's past business process input history and provide an optimal input interface. Examples of optimal input interfaces include, but are not limited to, GUI, voice input, and touch screen. For example, the reception unit can automatically display business processes that the user has frequently input in the past as candidates. The reception unit can also prioritize and suggest input methods (voice, text, etc.) that the user has used in the past. The reception unit can also predict and suggest business processes to be used in specific time periods based on the user's past input history. By analyzing the past input history, the reception unit can provide an optimal input interface for the user. Some or all of the above-described processing in the reception unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the reception unit can input the user's past input history data into a generation AI, which can then analyze the content and provide an optimal input interface.

[0062] When inputting a business process, the reception unit can filter the input content based on the user's current work situation and areas of interest. Examples of work situation include, but are not limited to, current tasks and project progress. Examples of areas of interest include, but are not limited to, specific business domains and technical fields. For example, the reception unit can prioritize displaying business processes related to a project currently being carried out by the user. The reception unit can also suggest related business processes based on the user's areas of interest. The reception unit can also filter and display optimal business processes taking into account the user's current work situation. This allows the user to input more relevant business processes by filtering the input content based on the user's work situation and areas of interest. Some or all of the above-described processing in the reception unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the reception unit can input data on the user's work situation and areas of interest into the generation AI, which can then analyze the data and filter the input content.

[0063] The reception unit can estimate the user's emotions and prioritize the input business processes based on the estimated user emotions. Emotions include, but are not limited to, joy, sadness, and anger. For example, when the user is stressed, the reception unit can prioritize displaying business processes with high importance. Furthermore, when the user is relaxed, the reception unit can prioritize displaying detailed business processes. Furthermore, when the user is in a hurry, the reception unit can prioritize displaying business processes that can be completed quickly. This allows business processes to be prioritized based on the user's emotions, resulting in more appropriate processing of the business processes. The emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using, for example, the generation AI. For example, the reception unit can input the user's emotion data into the generation AI, which can then analyze the data and prioritize the business processes.

[0064] When inputting business processes, the reception unit can prioritize inputting highly relevant business processes by taking into account the user's geographical location information. Examples of geographical location information include, but are not limited to, GPS data, IP addresses, and location information services. For example, when the user is in a specific location, the reception unit can prioritize displaying business processes related to that location. The reception unit can also prioritize displaying business processes to be performed in locations close to the user's current location. The reception unit can also suggest optimal business processes based on the user's geographical location information. This allows highly relevant business processes to be input preferentially by taking the user's geographical location information into consideration. Some or all of the above-described processing in the reception unit can be performed using, or without, a generation AI. For example, the reception unit can input the user's geographical location information data into the generation AI, which can then analyze the data and input highly relevant business processes preferentially.

[0065] The reception unit can analyze the user's social media activity when inputting a business process and input the related business process. Social media activity includes, but is not limited to, for example, the content of posts, the number of likes, and the number of followers. The reception unit, for example, can prioritize and display business processes mentioned by the user on social media. The reception unit can also suggest related business processes based on the user's social media activity. The reception unit can also display optimal business processes based on the user's social media interests. This allows the related business process to be input by analyzing the social media activity. Some or all of the above-described processing in the reception unit can be performed, for example, using a generation AI or without using a generation AI. For example, the reception unit can input the user's social media activity data to the generation AI, which can then analyze the content and input the related business process.

[0066] The generation unit can estimate the user's emotion and adjust the generation method of the action scenario based on the estimated user emotion. Emotions include, but are not limited to, joy, sadness, anger, etc. For example, when the user is relaxed, the generation unit can generate a detailed action scenario. Furthermore, when the user is in a hurry, the generation unit can generate a concise action scenario. Furthermore, when the user is excited, the generation unit can generate an action scenario with visually stimulating effects. By adjusting the generation method of the action scenario based on the user's emotion, a more appropriate scenario can be generated. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit can be performed using, for example, the generation AI, or without the generation AI. For example, the generation unit can input the user's emotion data into the generation AI, which can then analyze the data and adjust the generation method of the action scenario.

[0067] When generating an operational scenario, the generation unit can adjust the level of detail of the scenario based on the importance of the business process. Examples of importance include, but are not limited to, the priority level, impact level, and urgency of the business. For example, the generation unit generates a detailed operational scenario for a business process with high importance. The generation unit can also generate a concise operational scenario for a business process with low importance. The generation unit can also dynamically adjust the level of detail of the scenario according to the importance of the business process. This allows for the generation of a more appropriate scenario by adjusting the level of detail of the scenario based on the importance of the business process. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input business process importance data into the generation AI, which can then analyze the data and adjust the level of detail of the scenario.

[0068] When generating an operational scenario, the generation unit can apply different generation algorithms depending on the category of the business process. Examples of categories include, but are not limited to, the type of business process and the phase of the project. For example, the generation unit can apply a specific algorithm to a data entry task to generate an operational scenario. The generation unit can also apply a different algorithm to a data analysis task to generate an operational scenario. The generation unit can also select an optimal generation algorithm depending on the category of the business process to generate an operational scenario. By applying the optimal generation algorithm depending on the category of the business process, a more appropriate scenario can be generated. Some or all of the above-described processing in the generation unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the generation unit can input business process category data into the generation AI, which can analyze the data and apply the optimal generation algorithm.

[0069] The generation unit can estimate the user's emotions and adjust the length of the action scenario based on the estimated user emotions. Emotions include, but are not limited to, joy, sadness, anger, etc. For example, if the user is in a hurry, the generation unit can generate a short, concise action scenario. If the user is relaxed, the generation unit can generate a longer action scenario with detailed explanations. If the user is excited, the generation unit can generate an action scenario with visually stimulating effects. This allows for the generation of a more appropriate scenario by adjusting the length of the scenario based on the user's emotions. The emotion estimation is achieved using, for example, an emotion engine or a generation AI with an emotion estimation function. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the generation unit can be performed using, for example, the generation AI. For example, the generation unit can input the user's emotion data into the generation AI, which can then analyze the content and adjust the length of the scenario.

[0070] When generating operational scenarios, the generation unit can determine the priority of the scenarios based on the submission times of the business processes. Examples of submission times include, but are not limited to, submission deadlines and project schedules. For example, the generation unit can prioritize generating operational scenarios for business processes with upcoming submission deadlines. The generation unit can also postpone generating operational scenarios for business processes with more distant submission deadlines. The generation unit can also dynamically adjust the priority of scenarios based on the submission times of the business processes. By determining the priority of scenarios based on the submission times of the business processes, scenarios can be generated in a more appropriate order. 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 submission time data of business processes into the generation AI, which can then analyze the data and determine the priority of the scenarios.

[0071] When generating an operational scenario, the generation unit can adjust the order of the scenarios based on the relevance of the business processes. Examples of relevance include, but are not limited to, the degree of relevance of the business processes and project dependencies. For example, the generation unit prioritizes placing highly relevant business processes in the first half of the scenario. The generation unit can also place less relevant business processes in the second half of the scenario. The generation unit can also dynamically adjust the order of the scenarios based on the relevance of the business processes. By adjusting the order of the scenarios based on the relevance of the business processes, scenarios can be generated in a more appropriate order. 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 relevance data of the business processes into the generation AI, which can then analyze the data and adjust the order of the scenarios.

[0072] The creation unit can estimate the user's emotions and adjust the creation method of the RPA robot based on the estimated user emotions. Emotions include, but are not limited to, joy, sadness, anger, etc. For example, if the user is relaxed, the creation unit can create an RPA robot by providing detailed setting options. Alternatively, if the user is in a hurry, the creation unit can quickly create an RPA robot by providing simple setting options. Alternatively, if the user is excited, the creation unit can create an RPA robot with visually stimulating effects. This allows for the creation of a more appropriate robot by adjusting the creation method of the RPA robot based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, 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 creation unit can be performed using, for example, the generation AI. For example, the creation unit can input the user's emotion data into the generation AI, which can then analyze the content and adjust the creation method.

[0073] When creating an RPA robot, the creation unit can analyze the user's past business processes and select the optimal creation method. Past business processes include, but are not limited to, past task history and project deliverables. The creation unit can, for example, propose the optimal creation method based on data of RPA robots created by the user in the past. The creation unit can also analyze the user's past business processes and select an efficient creation method. The creation unit can also propose the optimal creation procedure based on the user's past business processes. In this way, the optimal creation method can be selected by analyzing the past business processes. Some or all of the above-mentioned processing in the creation unit can be performed, for example, using a generation AI or without using a generation AI. For example, the creation unit can input the user's past business process data into the generation AI, which can then analyze the content and select the optimal creation method.

[0074] When creating an RPA robot, the creation unit can customize the creation means based on the user's current work situation. The current work situation includes, but is not limited to, the user's current task and project progress. For example, the creation unit prioritizes the creation of an RPA robot related to a project currently underway by the user. The creation unit can also propose an optimal creation means taking into account the user's current work situation. The creation unit can also create a customized RPA robot based on the user's current work situation. This allows customizing the creation means based on the current work situation to create a more appropriate RPA robot. Some or all of the above-described processing in the creation unit may be performed using, or without, a generation AI. For example, the creation unit can input the user's current work situation data into the generation AI, which can then analyze the data and customize the creation means.

[0075] The creation unit can estimate the user's emotions and determine the creation priority of RPA robots based on the estimated user emotions. Emotions include, but are not limited to, joy, sadness, and anger. For example, if the user is stressed, the creation unit can prioritize the creation of RPA robots with high importance. Furthermore, if the user is relaxed, the creation unit can prioritize the creation of RPA robots that require detailed configuration. Furthermore, if the user is in a hurry, the creation unit can prioritize the creation of RPA robots that can be created quickly. This allows the creation of RPA robots in a more appropriate order by determining the creation priority based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the creation unit can be performed using, for example, the generation AI. For example, the creation unit can input the user's emotion data into the generation AI, which can then analyze the content and determine the creation priority.

[0076] When creating an RPA robot, the creation unit can select the optimal creation method by taking into account the user's geographical location information. Examples of geographical location information include, but are not limited to, GPS data, IP addresses, and location information services. For example, if the user is in a specific location, the creation unit prioritizes creating an RPA robot related to that location. The creation unit can also create an RPA robot corresponding to a business process performed in a location close to the user's current location. The creation unit can also suggest the optimal creation method based on the user's geographical location information. This allows for the selection of a more appropriate creation method by taking the geographical location information into consideration. Some or all of the above-described processing in the creation unit may be performed using, or without, a generation AI. For example, the creation unit can input the user's geographical location information data into the generation AI, which can then analyze the data and select the optimal creation method.

[0077] When creating an RPA robot, the creation unit can analyze a user's social media activity and suggest a creation method. Social media activity includes, but is not limited to, post content, the number of likes, and the number of followers. For example, the creation unit creates an RPA robot corresponding to a business process mentioned by the user on social media. The creation unit can also suggest a creation method for a related RPA robot based on the user's social media activity. The creation unit can also suggest an optimal creation method based on the user's social media interests. In this way, analyzing social media activity can suggest a more appropriate creation method. Some or all of the above-described processing in the creation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the creation unit can input the user's social media activity data into the generation AI, which can then analyze the content and suggest a creation method. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, generation unit, and creation unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart device 14 and receives business process input from an end user. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the business process input using a generation AI to generate an operation scenario. The creation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and creates an RPA robot based on the generated operation scenario. === Hard Collateral 1-2 === Each of the multiple elements including the above-described reception unit, generation unit, and creation unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart glasses 214 and receives business process input from an end user. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the business process input using a generation AI to generate an operation scenario. The creation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and creates an RPA robot based on the generated operation scenario. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, generation unit, and creation unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the headset-type terminal 314 and receives business process input from an end user. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the business process input using a generation AI to generate an operation scenario. The creation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and creates an RPA robot based on the generated operation scenario. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, generation unit, and creation unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the robot 414 and receives business process input from an end user. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the business process input using a generation AI to generate an operation scenario. The creation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and creates an RPA robot based on the generated operation scenario.

[0078] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0079] The reception unit can analyze the user's past input history and automatically suggest frequently used business processes. For example, if the user has frequently input the business process "Create meeting minutes every Monday" in the past, the reception unit will automatically display that business process as a candidate. It can also prioritize suggestions based on input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest business processes that will be used during specific time periods based on the user's past input history. In this way, by analyzing the user's past input history, it is possible to provide the user with the optimal input interface.

[0080] When generating an operational scenario, the generation unit can adjust the level of detail of the scenario based on the importance of the business process. For example, a detailed operational scenario can be generated for a business process with high importance, and a simple operational scenario can be generated for a business process with low importance. The generation unit can also dynamically adjust the level of detail of the scenario according to the importance of the business process. This allows for the generation of a more appropriate scenario by adjusting the level of detail of the scenario based on the importance of the business process.

[0081] When generating an operational scenario, the generation unit can apply different generation algorithms depending on the business process category. For example, a specific algorithm can be applied to generate an operational scenario for a data entry task, and a different algorithm can be applied to generate an operational scenario for a data analysis task. The generation unit can also select the optimal generation algorithm depending on the business process category to generate an operational scenario. This makes it possible to generate a more appropriate scenario by applying the optimal generation algorithm depending on the business process category.

[0082] When creating an RPA robot, the creation unit can analyze the user's past business processes and select the optimal creation method. For example, the creation unit can suggest the optimal creation method based on data from RPA robots created by the user in the past. The creation unit can also analyze the user's past business processes and select an efficient creation method. Furthermore, the creation unit can suggest the optimal creation procedure based on the user's past business processes. This makes it possible to select the optimal creation method by analyzing past business processes.

[0083] When creating an RPA robot, the creation unit can customize the creation method based on the user's current work situation. For example, the creation unit can prioritize the creation of an RPA robot related to a project currently underway by the user. The creation unit can also propose the optimal creation method taking into account the user's current work situation. Furthermore, the creation unit can create a customized RPA robot based on the user's current work situation. This allows the creation of a more appropriate RPA robot by customizing the creation method based on the current work situation.

[0084] The reception unit can estimate the user's emotions and adjust the business process input method based on the estimated user emotions. For example, if the user is feeling stressed, a simple interface can be provided to minimize the input steps. Alternatively, if the user is relaxed, detailed input options can be provided and a customizable input method can be suggested. Furthermore, if the user is in a hurry, voice input can be prioritized to enable the user to input business processes quickly. In this way, adjusting the input method according to the user's emotions enables more appropriate business process input.

[0085] The reception unit can estimate the user's emotions and determine the priority of the input business processes based on the estimated user emotions. For example, if the user is feeling stressed, business processes with high importance can be displayed with priority. Also, if the user is relaxed, detailed business processes can be displayed with priority. Furthermore, if the user is in a hurry, business processes that can be completed quickly can be displayed with priority. Thus, by determining the priority of business processes based on the user's emotions, business processes can be processed in a more appropriate order.

[0086] The generation unit can estimate the user's emotions and adjust the generation method of the action scenario based on the estimated user's emotions. For example, if the user is relaxed, a detailed action scenario can be generated. If the user is in a hurry, a concise action scenario can be generated. Furthermore, if the user is excited, an action scenario with visually stimulating effects can be generated. In this way, by adjusting the generation method of the action scenario based on the user's emotions, more appropriate scenarios can be generated.

[0087] The generation unit can estimate the user's emotions and adjust the length of the action scenario based on the estimated user's emotions. For example, if the user is in a hurry, a short action scenario that is to the point can be generated. If the user is relaxed, a longer action scenario that includes detailed explanations can be generated. Furthermore, if the user is excited, an action scenario that includes visually stimulating effects can be generated. In this way, by adjusting the length of the scenario based on the user's emotions, more appropriate scenarios can be generated.

[0088] The creation unit can estimate the user's emotions and adjust the creation method of the RPA robot based on the estimated user's emotions. For example, if the user is relaxed, the creation unit can provide detailed setting options to create an RPA robot. If the user is in a hurry, the creation unit can provide simple setting options to quickly create an RPA robot. Furthermore, if the user is excited, the creation unit can create an RPA robot with visually stimulating effects. This allows the creation of a more appropriate robot by adjusting the creation method of the RPA robot based on the user's emotions.

[0089] The processing flow of the second embodiment will be briefly explained below.

[0090] Step 1: The reception unit accepts business process input from the end user. For example, the end user can input a business process in natural language, such as "Check email every day at 9:00 and move emails containing specific keywords to a folder." The reception unit passes this input to the generation AI. Step 2: The generation unit uses a generation AI to analyze the business process input received by the reception unit and generate an operation scenario. The generation AI analyzes the input business process using, for example, natural language processing technology and generates specific steps. For example, steps such as "check email," "search for specific keywords," and "move relevant emails to a folder" are generated. The generation unit passes the generated operation scenario to the creation unit. Step 3: The creation unit creates an RPA robot based on the operation scenario generated by the generation unit. For example, the RPA robot follows the generated procedure to check emails, search for specific keywords, and move relevant emails to a folder.

[0091] 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.

[0092] 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 the generative AI include a neural network (NN) and a neural network (NN). 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 (e.g., still image data or video data). 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 one or more data formats of voice data, text data, image data, etc. 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 may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0093] 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.

[0094] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0095] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0096] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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).

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0109] 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.

[0110] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0111] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0112] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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).

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0125] 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.

[0126] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0127] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0142] 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.

[0143] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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).

[0148] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0149] 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."

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] [Explanation of symbols]

[0163] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit that receives input of business processes from end users; a generation unit that analyzes the business process input received by the reception unit and generates an operation scenario; a creation unit that creates an RPA robot based on the operation scenario generated by the creation unit. A system characterized by:

2. The reception unit End users input the business process they want to automate in natural language The system of claim 1 .

3. The generation unit Using the generation AI, the input of the business process received by the reception unit is analyzed and an operation scenario is generated. The system of claim 1 .

4. The generation unit Generative AI generates specific steps to automate business processes The system of claim 1 .

5. The creation unit Creating an RPA robot based on the operation scenario generated by the generation unit The system of claim 1 .

6. The reception unit Inferring user emotions and adjusting the input method for business processes based on the inferred user emotions The system of claim 1 .

7. The reception unit Analyzes the user's past business process input history and provides the optimal input interface The system of claim 1 .

8. The reception unit When entering business processes, filter the input based on the user's current work situation or area of ​​interest. The system of claim 1 .

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