system

Abstract

The system according to this embodiment aims to automatically perform various tasks based on verbal instructions from the user. [Solution] The system according to the embodiment comprises a reception unit, an analysis unit, an execution unit, and a provision unit. The reception unit receives verbal instructions from the user. The analysis unit analyzes the instructions received by the reception unit and converts them into specific tasks. The execution unit performs various tasks based on the instructions analyzed by the analysis unit. The provision unit provides the user with the results of the tasks performed by the execution 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] Conventional technologies have not adequately automated various tasks based on verbal instructions, leaving room for improvement in efficiency.

[0005] The system according to this embodiment aims to automatically perform various tasks based on verbal instructions from the user. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, an execution unit, and a provision unit. The reception unit receives verbal instructions from a user. The analysis unit analyzes the instructions received by the reception unit and converts them into specific tasks. The execution unit executes various tasks based on the instructions analyzed by the analysis unit. The provision unit provides the user with the results of the tasks executed by the execution unit. [Effects of the Invention]

[0007] The system according to this embodiment can automatically perform various tasks based on verbal instructions from the user. [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 of form 1) A system according to an embodiment of the present invention uses AI to complete pre-order activities, such as creating meeting minutes, linking with a sales force automation (SFA), obtaining approval from an approval system (decision making), and creating application forms, through verbal communication. In this system, the AI ​​understands verbal instructions from a user and automatically executes the tasks. Behind the scenes, RPA (Robotic Process Automation) is used to execute tasks within each system and link between systems. For example, if a user issues an instruction to "create meeting minutes," the AI ​​understands the instruction and automatically creates the minutes. Next, if the user issues an instruction to "link with the sales force automation system," the AI ​​inputs the necessary information into the sales force automation system and performs the link. Furthermore, if the user issues an instruction to "obtain approval," the AI ​​accesses the approval system and performs the necessary procedures. Finally, if the user issues an instruction to "create an application form," the AI ​​automatically creates the application form. This system utilizes RPA to execute tasks within each system and link between systems. For example, if a user issues an instruction to create meeting minutes, the RPA accesses the minutes-creation system, inputs the necessary information, and creates the minutes. Similarly, RPA automatically performs tasks such as connecting with the sales support system, obtaining approvals, and creating application forms. This system allows users to complete various pre-order tasks simply by giving verbal instructions. This improves work efficiency and speeds up operations. Furthermore, by utilizing RPA, tasks in each system and the connections between systems are automated, reducing human error. This allows the system to efficiently complete pre-order activities by accepting, analyzing, and executing the user's verbal instructions and providing the results.

[0029] The system according to the embodiment includes a reception unit, an analysis unit, an execution unit, and a provision unit. The reception unit receives verbal instructions from a user. The verbal instructions from the user include, but are not limited to, voice commands and instructions in natural language. The reception unit receives the user's instructions using, for example, voice recognition technology. The reception unit can also analyze the user's instructions using natural language processing technology. For example, the reception unit converts voice commands into text and analyzes the text. The analysis unit analyzes the instructions received by the reception unit and converts them into specific tasks. The analysis unit analyzes the instructions using, for example, natural language processing technology. The analysis unit can also analyze the instructions using AI. For example, the analysis unit analyzes the content of the instructions and generates specific work procedures. The execution unit performs various tasks based on the instructions analyzed by the analysis unit. The execution unit performs tasks using, for example, RPA. The execution unit can also perform tasks in cooperation with various systems. For example, the execution unit accesses a minutes-taking system and creates minutes. The providing unit provides the user with the work results executed by the executing unit. For example, the providing unit provides the work results as a text report or a graph. The providing unit can also provide feedback on the work results to the user. For example, the providing unit displays the work results on the user's device. In this way, the system according to the embodiment can efficiently complete pre-order activities by accepting, analyzing, and executing the user's verbal instructions and providing the results.

[0030] The execution unit can cooperate with a minutes-taking system, a sales support system, an approval system, and an application form preparation system to automatically perform various tasks. The execution unit, for example, accesses the minutes-taking system to prepare minutes. For example, the execution unit inputs necessary information into the minutes-taking system and automatically prepares minutes. The execution unit can also access the sales support system and input necessary information into the sales support system. For example, the execution unit inputs customer information into the sales support system and cooperates with the sales support system. The execution unit can also access the approval system and perform approval procedures. For example, the execution unit inputs necessary information into the approval system and obtains approval. The execution unit can also access the application form preparation system and prepare an application form. For example, the execution unit inputs necessary information into the application form preparation system and automatically prepares an application form. As a result, the execution unit can cooperate with each system to automatically perform various pre-order tasks. Some or all of the above-described processing in the execution unit may be performed using, for example, RPA, or may be performed without using RPA. For example, the execution unit accesses the meeting minutes creation system, inputs the information needed to create the meeting minutes into the RPA, and the RPA automatically creates the meeting minutes.

[0031] The reception desk can analyze the user's past instruction history and select the optimal reception method. For example, the reception desk can automatically display instructions that the user has frequently given in the past as candidates. For example, the reception desk can store the user's past instruction history in a database and prioritize displaying frequently given instructions. The reception desk can also prioritize suggesting reception methods (voice, text, etc.) that the user has used in the past. For example, the reception desk can use an algorithm to analyze the user's past instruction history and select the optimal reception method. The reception desk can also predict and suggest instructions to be used during specific time periods based on the user's past instruction history. For example, the reception desk can analyze the user's past instruction history chronologically and predict instructions that are frequently given during specific time periods. In this way, by analyzing past instruction history, the optimal reception method can be provided. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's past instruction history into AI and have the AI ​​select the optimal reception method.

[0032] The reception desk can filter verbal instructions based on the user's current projects and areas of interest. For example, the reception desk can prioritize instructions related to the user's current projects. For instance, it can store the user's project information in a database and filter relevant instructions. The reception desk can also filter and accept relevant instructions based on the user's areas of interest. For example, it can store the user's areas of interest as profile information and prioritize relevant instructions. The reception desk can also suggest appropriate instructions based on the progress of the user's projects. For example, it can monitor project progress in real time and suggest appropriate instructions. This allows for the priority of receiving highly relevant instructions by filtering them based on the current project and areas of interest. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input the user's project information into an AI and have the AI ​​perform the filtering of relevant instructions.

[0033] When accepting verbal instructions, the reception unit can prioritize accepting highly relevant instructions by taking into account the user's geographical location information. For example, when the user is in a specific location, the reception unit prioritizes accepting instructions related to that location. For example, the reception unit acquires the user's geographical location information from GPS data and filters instructions related to that location. The reception unit can also prioritize accepting instructions related to locations close to the user's current location. For example, the reception unit can suggest relevant instructions based on the user's current location. The reception unit can also suggest optimal instructions based on the user's geographical location information. For example, the reception unit can monitor the user's geographical location information in real time and suggest optimal instructions. In this way, highly relevant instructions can be prioritized by taking the geographical location information into consideration. Some or all of the above-described processing in the reception unit may be performed using, or without, AI. For example, the reception unit can input the user's geographical location information to AI and have the AI ​​filter relevant instructions.

[0034] The reception desk can analyze the user's social media activity and receive relevant instructions when receiving verbal instructions. For example, the reception desk can prioritize receiving relevant instructions based on the content of the user's social media activity. For example, the reception desk can analyze the user's social media accounts and filter relevant instructions. The reception desk can also suggest appropriate instructions based on the user's interests on social media. For example, the reception desk can analyze the content of the user's social media posts and suggest relevant instructions. The reception desk can also receive relevant instructions based on the user's social media activity history. For example, the reception desk can store the user's social media activity history in a database and filter relevant instructions. This allows the reception desk to prioritize receiving relevant instructions by analyzing social media activity. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input the user's social media activity into AI and have the AI ​​perform the filtering of relevant instructions.

[0035] The analysis unit can adjust the level of detail of the analysis based on the importance of the instructions during instruction analysis. For example, the analysis unit can perform a detailed analysis for important instructions to provide highly accurate results. For example, the analysis unit can evaluate the importance of instructions and perform a detailed analysis for important instructions. The analysis unit can also perform a simplified analysis for less important instructions to provide results quickly. For example, the analysis unit can optimally allocate analysis resources based on the importance of the instructions. This enables efficient analysis by adjusting the level of detail of the analysis according to the importance of the instructions. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the instructions into the AI ​​and have the AI ​​adjust the level of detail of the analysis.

[0036] The analysis unit can apply different analysis algorithms depending on the category of the instruction when analyzing instructions. For example, the analysis unit can apply a natural language processing algorithm to an instruction to create meeting minutes. For example, the analysis unit analyzes an instruction to create meeting minutes and performs the analysis using a natural language processing algorithm. The analysis unit can also apply a database manipulation algorithm to an instruction to link with a sales support system. For example, the analysis unit analyzes an instruction to link with a sales support system and performs the analysis using a database manipulation algorithm. The analysis unit can also apply a workflow management algorithm to an instruction to obtain approval. For example, the analysis unit analyzes an instruction to obtain approval and performs the analysis using a workflow management algorithm. By applying the appropriate analysis algorithm according to the category of the instruction, the accuracy of the analysis is improved. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the category of the instruction into the AI ​​and have the AI ​​execute the application of the appropriate analysis algorithm.

[0037] The analysis unit can determine the priority of analyses based on the timing of instruction submissions during instruction analysis. For example, the analysis unit can prioritize urgent instructions and provide results quickly. For example, the analysis unit can evaluate the timing of instruction submissions and prioritize analyses for urgent instructions. The analysis unit can also postpone analyses of older instructions. For example, the analysis unit can optimally allocate analysis resources based on the timing of instruction submissions. This enables efficient analysis by determining the priority of analyses based on the submission timing. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the timing of instruction submissions into the AI ​​and have the AI ​​determine the priority of analyses.

[0038] The analysis unit can adjust the order of analysis based on the relevance of the instructions during instruction analysis. For example, the analysis unit can prioritize the analysis of highly relevant instructions and provide results quickly. For example, the analysis unit can evaluate the relevance of the instructions and prioritize the analysis of highly relevant instructions. The analysis unit can also postpone the analysis of less relevant instructions. For example, the analysis unit can optimally allocate analysis resources based on the relevance of the instructions. This allows for efficient analysis by adjusting the order of analysis based on relevance. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of the instructions into the AI ​​and have the AI ​​adjust the order of analysis.

[0039] The execution unit can improve the accuracy of execution by considering the interrelationships between tasks during execution. For example, the execution unit can link the tasks of creating meeting minutes and integrating with the sales support system to maintain data consistency. For example, the execution unit can synchronize data between the meeting minutes creation system and the sales support system. The execution unit can also streamline procedures by linking the tasks of obtaining approvals and creating application forms. For example, the execution unit can link data between the approval system and the application form creation system. Furthermore, the execution unit can analyze the interrelationships between each task and execute them in the optimal order. For example, the execution unit can evaluate the dependencies between each task and determine the optimal execution order. This improves the accuracy of execution by considering the interrelationships between tasks. Some or all of the above processing in the execution unit may be performed using RPA, for example, or without RPA. For example, the execution unit can input the interrelationships between each task into RPA and have RPA perform the task of improving execution accuracy.

[0040] When executing a task, the execution unit can take into account the attribute information of the task submitter. For example, if the submitter is a senior manager, the execution unit prioritizes the execution of the task. For example, the execution unit stores the submitter's job title in a database and prioritizes the execution of instructions from senior managers. Furthermore, if the submitter is a new employee, the execution unit can execute the task by following detailed procedures. For example, the execution unit stores the submitter's experience information in a database and provides detailed procedures for instructions from new employees. Furthermore, the execution unit can execute the task using the optimal procedure depending on the submitter's job title and experience. For example, the execution unit determines the optimal work procedure based on the submitter's attribute information. This enables more appropriate task execution by taking the submitter's attribute information into consideration. Some or all of the above-described processing in the execution unit may be performed using, for example, an RPA, or may be performed without an RPA. For example, the execution unit can input the submitter's attribute information into an RPA and have the RPA execute the task.

[0041] The execution unit can take the geographical distribution of tasks into consideration when executing tasks. For example, when a user is in a specific location, the execution unit prioritizes tasks related to that location. For example, the execution unit obtains the user's geographical location information from GPS data and filters tasks related to that location. The execution unit can also prioritize tasks related to locations close to the user's current location. For example, the execution unit suggests related tasks based on the user's current location. The execution unit can also suggest optimal tasks based on the user's geographical location information. For example, the execution unit monitors the user's geographical location information in real time and suggests optimal tasks. This allows highly relevant tasks to be prioritized by taking the geographical distribution into consideration. Some or all of the above-described processing in the execution unit may be performed using, or without, an RPA. For example, the execution unit can input the user's geographical location information into an RPA and have the RPA filter relevant tasks.

[0042] The execution unit can improve the accuracy of its work by referring to relevant documentation during task execution. For example, when creating meeting minutes, the execution unit can improve accuracy by referring to relevant past meeting minutes. For example, the execution unit can retrieve past meeting minutes from a database and incorporate them into the meeting minute creation process. The execution unit can also improve accuracy by referring to relevant sales materials when integrating with a sales support system. For example, the execution unit can retrieve sales materials from a database and incorporate them into the sales support system integration process. The execution unit can also improve accuracy by referring to relevant approval documents when obtaining approvals. For example, the execution unit can retrieve approval documents from a database and incorporate them into the approval process. In this way, the accuracy of the work is improved by referring to relevant documentation. Some or all of the above processes in the execution unit may be performed using RPA, for example, or without using RPA. For example, the execution unit can input relevant documentation into RPA and have RPA perform the task of improving execution accuracy.

[0043] The service provider can select the optimal display method by referring to the user's past operation history when providing work results. For example, the service provider can prioritize providing display methods that the user has preferred to use in the past. For example, the service provider can store the user's past operation history in a database and prioritize providing preferred display methods. The service provider can also propose the most efficient display method based on the user's past operation history. For example, the service provider can analyze the user's past operation history and provide the optimal display layout. The service provider can also select the optimal display method based on the user's past operation history. For example, the service provider can analyze the user's past operation history chronologically and select the optimal display method. This allows the service provider to provide the optimal display method by referring to past operation history. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's past operation history into AI and have the AI ​​select the optimal display method.

[0044] When providing work results, the providing unit may filter the work results based on the user's current project or areas of interest. For example, the providing unit may preferentially display work results related to the user's current project. For example, the providing unit may store the user's project information in a database and filter related work results. The providing unit may also filter and display related work results based on the user's areas of interest. For example, the providing unit may store the user's areas of interest as profile information and preferentially display related work results. The providing unit may also suggest appropriate work results based on the progress of the user's project. For example, the providing unit may monitor the progress of the project in real time and suggest appropriate work results. This allows for highly relevant results to be provided by filtering the work results based on the current project or areas of interest. Some or all of the above-described processing in the providing unit may be performed using, or without, AI. For example, the providing unit may input the user's project information into AI and have the AI ​​filter related work results.

[0045] When providing work results, the providing unit can prioritize providing highly relevant results by taking into account the user's geographical location information. For example, if the user is in a specific location, the providing unit prioritizes displaying work results related to that location. For example, the providing unit obtains the user's geographical location information from GPS data and filters work results related to that location. The providing unit can also prioritize displaying work results related to locations close to the user's current location. For example, the providing unit can suggest relevant work results based on the user's current location. The providing unit can also suggest optimal work results based on the user's geographical location information. For example, the providing unit can monitor the user's geographical location information in real time and suggest optimal work results. This allows highly relevant results to be prioritized by taking the geographical location information into consideration. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or without AI. For example, the providing unit can input the user's geographical location information to AI and have the AI ​​filter relevant work results.

[0046] The service provider can analyze the user's social media activity and provide relevant results when delivering work results. For example, the service provider can prioritize displaying relevant work results based on the user's social media activity. For example, the service provider can analyze the user's social media accounts and filter relevant work results. The service provider can also suggest appropriate work results based on the user's social media interests. For example, the service provider can analyze the user's social media posts and suggest relevant work results. The service provider can also provide relevant work results based on the user's social media activity history. For example, the service provider can store the user's social media activity history in a database and filter relevant work results. This allows the service provider to prioritize providing relevant results by analyzing social media activity. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the user's social media activity into AI and have the AI ​​perform the filtering of relevant work results.

[0047] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0048] The execution unit can monitor the progress of tasks in real time and automatically adjust the next task according to the progress. For example, once the creation of meeting minutes is completed, it can start the next task, the integration with the sales support system. It can also temporarily suspend the application form creation task if approval is delayed. Furthermore, the execution unit can visualize the progress of each task and report it to the user in real time. This enables flexible responses according to the progress of the tasks.

[0049] The reception unit can automatically provide related additional information based on the user's instructions. For example, if the user instructs "Create minutes," detailed information about the related meeting is displayed. Also, if the user instructs "Link with the sales support system," related customer information can be displayed. Furthermore, the reception unit can also display a history of related past instructions based on the user's instructions. This makes it possible to provide related information based on the user's instructions.

[0050] The reception unit monitors the user's current activity status and can receive instructions at an appropriate timing. For example, if the user is in a meeting, the reception of instructions can be suspended until the meeting ends. Also, if the user is traveling, the reception of instructions can be delayed until the user has completed traveling. Furthermore, the reception unit can adjust the method of receiving instructions according to the user's activity status. This allows for flexible instruction reception according to the user's activity status.

[0051] When analyzing instructions, the analysis unit can improve the accuracy of the analysis by referring to the user's past instruction history. For example, when analyzing instructions similar to those issued by the user in the past, the analysis unit refers to the past analysis results. It can also extract specific patterns from the user's past instruction history and optimize the analysis algorithm. Furthermore, the analysis unit can analyze predicted instruction content in advance based on the user's past instruction history. This enables highly accurate analysis by utilizing the past instruction history.

[0052] When analyzing instructions, the analysis unit can adjust the analysis algorithm based on the user's current project or area of ​​interest. For example, when analyzing instructions related to a project currently being carried out by the user, an algorithm specialized for the project can be applied. The analysis unit can also prioritize analysis of related information based on the user's area of ​​interest. Furthermore, the analysis unit can dynamically adjust the analysis algorithm according to the progress of the user's project. This enables appropriate analysis based on the user's current project or area of ​​interest.

[0053] When analyzing instructions, the analysis unit can take into account the attribute information of the person submitting the instructions. For example, if the submitter is a senior manager, a detailed analysis will be performed to provide highly accurate results. On the other hand, if the submitter is a new employee, it can provide simple and easy-to-understand analysis results. Furthermore, the analysis unit can adjust the analysis algorithm according to the submitter's position and experience. This enables appropriate analysis based on the submitter's attribute information.

[0054] The processing flow of the first embodiment will be briefly explained below.

[0055] Step 1: The reception unit receives verbal instructions from the user. The verbal instructions from the user include voice commands and instructions in natural language. The reception unit may receive the user's instructions using voice recognition technology and may also analyze the instructions using natural language processing technology. For example, the reception unit may convert the voice command into text and analyze the text. Step 2: The analysis unit analyzes the instructions received by the reception unit and converts them into specific tasks. The analysis unit uses natural language processing technology and AI to analyze the instructions and generate specific work procedures. Step 3: The execution unit executes various tasks based on the instructions analyzed by the analysis unit. The execution unit executes tasks using RPA and can also execute tasks in cooperation with various systems. For example, it accesses a minutes-taking system and creates minutes. Step 4: The providing unit provides the results of the work performed by the executing unit to the user. The providing unit may provide the results of the work as a text report or graph, and may display the results on the user's device.

[0056] (Example 2) A system according to an embodiment of the present invention uses AI to complete pre-order activities, such as creating meeting minutes, linking with a sales force automation (SFA), obtaining approval from an approval system (decision making), and creating application forms, through verbal communication. In this system, the AI ​​understands verbal instructions from a user and automatically executes the tasks. Behind the scenes, RPA (Robotic Process Automation) is used to execute tasks within each system and link between systems. For example, if a user issues an instruction to "create meeting minutes," the AI ​​understands the instruction and automatically creates the minutes. Next, if the user issues an instruction to "link with the sales force automation system," the AI ​​inputs the necessary information into the sales force automation system and performs the link. Furthermore, if the user issues an instruction to "obtain approval," the AI ​​accesses the approval system and performs the necessary procedures. Finally, if the user issues an instruction to "create an application form," the AI ​​automatically creates the application form. This system utilizes RPA to execute tasks within each system and link between systems. For example, if a user issues an instruction to create meeting minutes, the RPA accesses the minutes-creation system, inputs the necessary information, and creates the minutes. Similarly, RPA automatically performs tasks such as connecting with the sales support system, obtaining approvals, and creating application forms. This system allows users to complete various pre-order tasks simply by giving verbal instructions. This improves work efficiency and speeds up operations. Furthermore, by utilizing RPA, tasks in each system and the connections between systems are automated, reducing human error. This allows the system to efficiently complete pre-order activities by accepting, analyzing, and executing the user's verbal instructions and providing the results.

[0057] The system according to the embodiment includes a reception unit, an analysis unit, an execution unit, and a provision unit. The reception unit receives verbal instructions from a user. The verbal instructions from the user include, but are not limited to, voice commands and instructions in natural language. The reception unit receives the user's instructions using, for example, voice recognition technology. The reception unit can also analyze the user's instructions using natural language processing technology. For example, the reception unit converts voice commands into text and analyzes the text. The analysis unit analyzes the instructions received by the reception unit and converts them into specific tasks. The analysis unit analyzes the instructions using, for example, natural language processing technology. The analysis unit can also analyze the instructions using AI. For example, the analysis unit analyzes the content of the instructions and generates specific work procedures. The execution unit performs various tasks based on the instructions analyzed by the analysis unit. The execution unit performs tasks using, for example, RPA. The execution unit can also perform tasks in cooperation with various systems. For example, the execution unit accesses a minutes-taking system and creates minutes. The providing unit provides the user with the work results executed by the executing unit. For example, the providing unit provides the work results as a text report or a graph. The providing unit can also provide feedback on the work results to the user. For example, the providing unit displays the work results on the user's device. In this way, the system according to the embodiment can efficiently complete pre-order activities by accepting, analyzing, and executing the user's verbal instructions and providing the results.

[0058] The execution unit can cooperate with a minutes-taking system, a sales support system, an approval system, and an application form preparation system to automatically perform various tasks. The execution unit, for example, accesses the minutes-taking system to prepare minutes. For example, the execution unit inputs necessary information into the minutes-taking system and automatically prepares minutes. The execution unit can also access the sales support system and input necessary information into the sales support system. For example, the execution unit inputs customer information into the sales support system and cooperates with the sales support system. The execution unit can also access the approval system and perform approval procedures. For example, the execution unit inputs necessary information into the approval system and obtains approval. The execution unit can also access the application form preparation system and prepare an application form. For example, the execution unit inputs necessary information into the application form preparation system and automatically prepares an application form. As a result, the execution unit can cooperate with each system to automatically perform various pre-order tasks. Some or all of the above-described processing in the execution unit may be performed using, for example, RPA, or may be performed without using RPA. For example, the execution unit accesses the meeting minutes creation system, inputs the information needed to create the meeting minutes into the RPA, and the RPA automatically creates the meeting minutes.

[0059] The reception unit can estimate the user's emotions and adjust the method for accepting verbal instructions based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit can provide a simple and intuitive interface and quickly accept instructions. For example, the reception unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. Furthermore, if the user is relaxed, the reception unit can provide options for accepting detailed instructions and suggest a customizable reception method. For example, the reception unit can record the user's voice and estimate their emotions using voice analysis technology. Furthermore, if the user is in a hurry, the reception unit can prioritize voice input and quickly accept instructions. For example, the reception unit can collect the user's biometric data (heart rate and electrodermal activity) using a sensor and estimate their emotions using an emotion estimation algorithm. This allows for more appropriate instruction acceptance by adjusting the method for accepting instructions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit may input image data of a user captured by a camera into the generation AI and cause the generation AI to estimate the user's emotions.

[0060] The reception desk can analyze the user's past instruction history and select the optimal reception method. For example, the reception desk can automatically display instructions that the user has frequently given in the past as candidates. For example, the reception desk can store the user's past instruction history in a database and prioritize displaying frequently given instructions. The reception desk can also prioritize suggesting reception methods (voice, text, etc.) that the user has used in the past. For example, the reception desk can use an algorithm to analyze the user's past instruction history and select the optimal reception method. The reception desk can also predict and suggest instructions to be used during specific time periods based on the user's past instruction history. For example, the reception desk can analyze the user's past instruction history chronologically and predict instructions that are frequently given during specific time periods. In this way, by analyzing past instruction history, the optimal reception method can be provided. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's past instruction history into AI and have the AI ​​select the optimal reception method.

[0061] The reception desk can filter verbal instructions based on the user's current projects and areas of interest. For example, the reception desk can prioritize instructions related to the user's current projects. For instance, it can store the user's project information in a database and filter relevant instructions. The reception desk can also filter and accept relevant instructions based on the user's areas of interest. For example, it can store the user's areas of interest as profile information and prioritize relevant instructions. The reception desk can also suggest appropriate instructions based on the progress of the user's projects. For example, it can monitor project progress in real time and suggest appropriate instructions. This allows for the priority of receiving highly relevant instructions by filtering them based on the current project and areas of interest. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input the user's project information into an AI and have the AI ​​perform the filtering of relevant instructions.

[0062] The reception desk can estimate the user's emotions and determine the priority of instructions to accept based on the estimated emotions. For example, if the user is tense, the reception desk will prioritize important instructions. For example, the reception desk may capture the user's facial expression with a camera and estimate their emotions using an emotion estimation algorithm. The reception desk may also prioritize detailed instructions if the user is relaxed. For example, the reception desk may record the user's voice and estimate their emotions using voice analysis technology. The reception desk may also prioritize instructions that require quick processing if the user is in a hurry. For example, the reception desk may collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. This allows for more appropriate instruction acceptance by determining the priority of instructions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit may input image data of a user taken with a camera to the generation AI and cause the generation AI to estimate the user's emotions.

[0063] When accepting verbal instructions, the reception unit can prioritize accepting highly relevant instructions by taking into account the user's geographical location information. For example, when the user is in a specific location, the reception unit prioritizes accepting instructions related to that location. For example, the reception unit acquires the user's geographical location information from GPS data and filters instructions related to that location. The reception unit can also prioritize accepting instructions related to locations close to the user's current location. For example, the reception unit can suggest relevant instructions based on the user's current location. The reception unit can also suggest optimal instructions based on the user's geographical location information. For example, the reception unit can monitor the user's geographical location information in real time and suggest optimal instructions. In this way, highly relevant instructions can be prioritized by taking the geographical location information into consideration. Some or all of the above-described processing in the reception unit may be performed using, or without, AI. For example, the reception unit can input the user's geographical location information to AI and have the AI ​​filter relevant instructions.

[0064] The reception desk can analyze the user's social media activity and receive relevant instructions when receiving verbal instructions. For example, the reception desk can prioritize receiving relevant instructions based on the content of the user's social media activity. For example, the reception desk can analyze the user's social media accounts and filter relevant instructions. The reception desk can also suggest appropriate instructions based on the user's interests on social media. For example, the reception desk can analyze the content of the user's social media posts and suggest relevant instructions. The reception desk can also receive relevant instructions based on the user's social media activity history. For example, the reception desk can store the user's social media activity history in a database and filter relevant instructions. This allows the reception desk to prioritize receiving relevant instructions by analyzing social media activity. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input the user's social media activity into AI and have the AI ​​perform the filtering of relevant instructions.

[0065] The analysis unit can estimate the user's emotions and adjust the method of analyzing instructions based on the estimated emotions. For example, if the user is relaxed, the analysis unit can perform a detailed analysis and provide highly accurate results. For example, the analysis unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The analysis unit can also perform a rapid analysis and provide concise results if the user is in a hurry. For example, the analysis unit can record the user's voice and estimate their emotions using voice analysis technology. The analysis unit can also provide simple and intuitive analysis results if the user is stressed. For example, the analysis unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. This allows for more appropriate analysis results by adjusting the analysis method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input image data of a user taken with a camera to the generation AI and cause the generation AI to estimate the user's emotions.

[0066] The analysis unit can adjust the level of detail of the analysis based on the importance of the instructions during instruction analysis. For example, the analysis unit can perform a detailed analysis for important instructions to provide highly accurate results. For example, the analysis unit can evaluate the importance of instructions and perform a detailed analysis for important instructions. The analysis unit can also perform a simplified analysis for less important instructions to provide results quickly. For example, the analysis unit can optimally allocate analysis resources based on the importance of the instructions. This enables efficient analysis by adjusting the level of detail of the analysis according to the importance of the instructions. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the instructions into the AI ​​and have the AI ​​adjust the level of detail of the analysis.

[0067] The analysis unit can apply different analysis algorithms depending on the category of the instruction when analyzing instructions. For example, the analysis unit can apply a natural language processing algorithm to an instruction to create meeting minutes. For example, the analysis unit analyzes an instruction to create meeting minutes and performs the analysis using a natural language processing algorithm. The analysis unit can also apply a database manipulation algorithm to an instruction to link with a sales support system. For example, the analysis unit analyzes an instruction to link with a sales support system and performs the analysis using a database manipulation algorithm. The analysis unit can also apply a workflow management algorithm to an instruction to obtain approval. For example, the analysis unit analyzes an instruction to obtain approval and performs the analysis using a workflow management algorithm. By applying the appropriate analysis algorithm according to the category of the instruction, the accuracy of the analysis is improved. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the category of the instruction into the AI ​​and have the AI ​​execute the application of the appropriate analysis algorithm.

[0068] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit can perform a short, concise analysis. For example, the analysis unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The analysis unit can also perform a more detailed analysis and provide longer results if the user is relaxed. For example, the analysis unit can record the user's voice and estimate their emotions using voice analysis technology. The analysis unit can also perform a simple and intuitive analysis if the user is stressed. For example, the analysis unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. This allows for more appropriate analysis results by adjusting the length of the analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input image data of a user taken with a camera to the generation AI and cause the generation AI to estimate the user's emotions.

[0069] The analysis unit can determine the priority of analyses based on the timing of instruction submissions during instruction analysis. For example, the analysis unit can prioritize urgent instructions and provide results quickly. For example, the analysis unit can evaluate the timing of instruction submissions and prioritize analyses for urgent instructions. The analysis unit can also postpone analyses of older instructions. For example, the analysis unit can optimally allocate analysis resources based on the timing of instruction submissions. This enables efficient analysis by determining the priority of analyses based on the submission timing. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the timing of instruction submissions into the AI ​​and have the AI ​​determine the priority of analyses.

[0070] The analysis unit can adjust the order of analysis based on the relevance of the instructions during instruction analysis. For example, the analysis unit can prioritize the analysis of highly relevant instructions and provide results quickly. For example, the analysis unit can evaluate the relevance of the instructions and prioritize the analysis of highly relevant instructions. The analysis unit can also postpone the analysis of less relevant instructions. For example, the analysis unit can optimally allocate analysis resources based on the relevance of the instructions. This allows for efficient analysis by adjusting the order of analysis based on relevance. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of the instructions into the AI ​​and have the AI ​​adjust the order of analysis.

[0071] The execution unit can estimate the user's emotions and adjust the way it performs tasks based on those emotions. For example, if the user is relaxed, the execution unit can perform tasks following detailed steps. For instance, it might capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The execution unit can also perform tasks quickly if the user is in a hurry. For example, it might record the user's voice and estimate their emotions using voice analysis technology. Furthermore, if the user is stressed, the execution unit can perform tasks following simple and intuitive steps. For example, it might collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. This allows for more appropriate task execution by adjusting the method of execution according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the execution unit may be performed using AI, for example, or without AI. For example, the execution unit can input user image data captured by a camera into a generating AI and have the generating AI perform the estimation of the user's emotions.

[0072] The execution unit can improve the accuracy of execution by considering the interrelationships between tasks during execution. For example, the execution unit can link the tasks of creating meeting minutes and integrating with the sales support system to maintain data consistency. For example, the execution unit can synchronize data between the meeting minutes creation system and the sales support system. The execution unit can also streamline procedures by linking the tasks of obtaining approvals and creating application forms. For example, the execution unit can link data between the approval system and the application form creation system. Furthermore, the execution unit can analyze the interrelationships between each task and execute them in the optimal order. For example, the execution unit can evaluate the dependencies between each task and determine the optimal execution order. This improves the accuracy of execution by considering the interrelationships between tasks. Some or all of the above processing in the execution unit may be performed using RPA, for example, or without RPA. For example, the execution unit can input the interrelationships between each task into RPA and have RPA perform the task of improving execution accuracy.

[0073] When executing a task, the execution unit can take into account the attribute information of the task submitter. For example, if the submitter is a senior manager, the execution unit prioritizes the execution of the task. For example, the execution unit stores the submitter's job title in a database and prioritizes the execution of instructions from senior managers. Furthermore, if the submitter is a new employee, the execution unit can execute the task by following detailed procedures. For example, the execution unit stores the submitter's experience information in a database and provides detailed procedures for instructions from new employees. Furthermore, the execution unit can execute the task using the optimal procedure depending on the submitter's job title and experience. For example, the execution unit determines the optimal work procedure based on the submitter's attribute information. This enables more appropriate task execution by taking the submitter's attribute information into consideration. Some or all of the above-described processing in the execution unit may be performed using, for example, an RPA, or may be performed without an RPA. For example, the execution unit can input the submitter's attribute information into an RPA and have the RPA execute the task.

[0074] The execution unit can estimate the user's emotions and determine the priority of tasks to be performed based on the estimated emotions. For example, if the user is tense, the execution unit will prioritize important tasks. For instance, it might capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. Similarly, if the user is relaxed, the execution unit can prioritize detailed tasks. For example, it might record the user's voice and estimate their emotions using voice analysis technology. Furthermore, if the user is in a hurry, the execution unit can prioritize tasks that require quick processing. For example, it might collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. This allows for more appropriate task execution by prioritizing tasks according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the execution unit may be performed using AI, for example, or without AI. For example, the execution unit can input user image data captured by a camera into a generating AI and have the generating AI perform the estimation of the user's emotions.

[0075] The execution unit can take the geographical distribution of tasks into consideration when executing tasks. For example, when a user is in a specific location, the execution unit prioritizes tasks related to that location. For example, the execution unit obtains the user's geographical location information from GPS data and filters tasks related to that location. The execution unit can also prioritize tasks related to locations close to the user's current location. For example, the execution unit suggests related tasks based on the user's current location. The execution unit can also suggest optimal tasks based on the user's geographical location information. For example, the execution unit monitors the user's geographical location information in real time and suggests optimal tasks. This allows highly relevant tasks to be prioritized by taking the geographical distribution into consideration. Some or all of the above-described processing in the execution unit may be performed using, or without, an RPA. For example, the execution unit can input the user's geographical location information into an RPA and have the RPA filter relevant tasks.

[0076] The execution unit can improve the accuracy of its work by referring to relevant documentation during task execution. For example, when creating meeting minutes, the execution unit can improve accuracy by referring to relevant past meeting minutes. For example, the execution unit can retrieve past meeting minutes from a database and incorporate them into the meeting minute creation process. The execution unit can also improve accuracy by referring to relevant sales materials when integrating with a sales support system. For example, the execution unit can retrieve sales materials from a database and incorporate them into the sales support system integration process. The execution unit can also improve accuracy by referring to relevant approval documents when obtaining approvals. For example, the execution unit can retrieve approval documents from a database and incorporate them into the approval process. In this way, the accuracy of the work is improved by referring to relevant documentation. Some or all of the above processes in the execution unit may be performed using RPA, for example, or without using RPA. For example, the execution unit can input relevant documentation into RPA and have RPA perform the task of improving execution accuracy.

[0077] The providing unit can estimate the user's emotions and adjust the display method of the work results based on the estimated user emotions. For example, if the user is nervous, the providing unit provides a simple, highly visible display method. For example, the providing unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. Furthermore, if the user is relaxed, the providing unit can provide a display method that includes detailed information. For example, the providing unit records the user's voice and estimates the emotion using voice analysis technology. Furthermore, if the user is in a hurry, the providing unit can provide a display method that focuses on the main points. For example, the providing unit collects the user's biometric data (heart rate and electrodermal activity) with a sensor and estimates the emotion using an emotion estimation algorithm. This allows for adjusting the display method according to the user's emotions, thereby enabling more appropriate display of the work results. 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 providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit may input image data of a user taken with a camera to the generating AI and cause the generating AI to estimate the user's emotions.

[0078] The service provider can select the optimal display method by referring to the user's past operation history when providing work results. For example, the service provider can prioritize providing display methods that the user has preferred to use in the past. For example, the service provider can store the user's past operation history in a database and prioritize providing preferred display methods. The service provider can also propose the most efficient display method based on the user's past operation history. For example, the service provider can analyze the user's past operation history and provide the optimal display layout. The service provider can also select the optimal display method based on the user's past operation history. For example, the service provider can analyze the user's past operation history chronologically and select the optimal display method. This allows the service provider to provide the optimal display method by referring to past operation history. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's past operation history into AI and have the AI ​​select the optimal display method.

[0079] When providing work results, the providing unit may filter the work results based on the user's current project or areas of interest. For example, the providing unit may preferentially display work results related to the user's current project. For example, the providing unit may store the user's project information in a database and filter related work results. The providing unit may also filter and display related work results based on the user's areas of interest. For example, the providing unit may store the user's areas of interest as profile information and preferentially display related work results. The providing unit may also suggest appropriate work results based on the progress of the user's project. For example, the providing unit may monitor the progress of the project in real time and suggest appropriate work results. This allows for highly relevant results to be provided by filtering the work results based on the current project or areas of interest. Some or all of the above-described processing in the providing unit may be performed using, or without, AI. For example, the providing unit may input the user's project information into AI and have the AI ​​filter related work results.

[0080] The service provider can estimate the user's emotions and prioritize work results based on those emotions. For example, if the user is stressed, the service provider will prioritize displaying important work results. For example, the service provider can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The service provider can also prioritize displaying detailed work results if the user is relaxed. For example, the service provider can record the user's voice and estimate their emotions using voice analysis technology. The service provider can also prioritize displaying work results that require quick confirmation if the user is in a hurry. For example, the service provider can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. This allows for the provision of more appropriate results by prioritizing work results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit may input image data of a user taken with a camera to the generating AI and cause the generating AI to estimate the user's emotions.

[0081] When providing work results, the providing unit can prioritize providing highly relevant results by taking into account the user's geographical location information. For example, if the user is in a specific location, the providing unit prioritizes displaying work results related to that location. For example, the providing unit obtains the user's geographical location information from GPS data and filters work results related to that location. The providing unit can also prioritize displaying work results related to locations close to the user's current location. For example, the providing unit can suggest relevant work results based on the user's current location. The providing unit can also suggest optimal work results based on the user's geographical location information. For example, the providing unit can monitor the user's geographical location information in real time and suggest optimal work results. This allows highly relevant results to be prioritized by taking the geographical location information into consideration. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or without AI. For example, the providing unit can input the user's geographical location information to AI and have the AI ​​filter relevant work results.

[0082] The service provider can analyze the user's social media activity and provide relevant results when delivering work results. For example, the service provider can prioritize displaying relevant work results based on the user's social media activity. For example, the service provider can analyze the user's social media accounts and filter relevant work results. The service provider can also suggest appropriate work results based on the user's social media interests. For example, the service provider can analyze the user's social media posts and suggest relevant work results. The service provider can also provide relevant work results based on the user's social media activity history. For example, the service provider can store the user's social media activity history in a database and filter relevant work results. This allows the service provider to prioritize providing relevant results by analyzing social media activity. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the user's social media activity into AI and have the AI ​​perform the filtering of relevant work results. === Hard Collateral 1-1 === Each of the multiple elements, including the above-mentioned reception unit, analysis unit, execution unit, and provision unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit receives a user's verbal instructions using the microphone 38B of the smart device 14 and converts the speech into text using the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the instructions using natural language processing technology, and generates specific work procedures. The execution unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and executes various tasks using RPA. The provision unit provides the work results to the user, for example, using the display 40A of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements, including the above-mentioned reception unit, analysis unit, execution unit, and provision unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit receives a user's verbal instructions using the microphone 238 of the smart glasses 214 and converts the speech into text using the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the instructions using natural language processing technology, and generates specific work procedures. The execution unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and executes various tasks using RPA. The provision unit provides the work results to the user, for example, using the display of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, execution unit, and provision unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit receives verbal instructions from the user using the microphone 238 of the headset-type terminal 314 and converts the speech into text using the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the instructions using natural language processing technology, and generates specific work procedures. The execution unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and executes various tasks using RPA. The provision unit provides the work results to the user using, for example, the display 343 of the headset-type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, execution unit, and provision unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit receives a user's verbal instructions using the microphone 238 of the robot 414 and converts the speech into text using the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the instructions using natural language processing technology, and generates specific work procedures. The execution unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and executes various tasks using RPA. The provision unit provides the work results to the user using, for example, the display of the robot 414.

[0083] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0084] The reception unit can analyze the tone and speed of the user's voice and estimate the user's level of urgency. For example, if the user's voice is high-pitched and fast, it can determine that the level of urgency is high and respond quickly. On the other hand, if the user's voice is low and slow, it can determine that the level of urgency is low and receive detailed instructions. Furthermore, the reception unit can monitor changes in the user's voice in real time and adjust the response according to changes in the level of urgency. This allows for more appropriate instruction reception based on the tone and speed of the user's voice.

[0085] The execution unit can monitor the progress of tasks in real time and automatically adjust the next task according to the progress. For example, once the creation of meeting minutes is completed, it can start the next task, the integration with the sales support system. It can also temporarily suspend the application form creation task if approval is delayed. Furthermore, the execution unit can visualize the progress of each task and report it to the user in real time. This enables flexible responses according to the progress of the tasks.

[0086] The reception unit can learn the user's past emotional data and predict the user's emotional patterns. For example, if the user is prone to feeling stressed during a certain time of day, a simple interface can be provided for that time of day. Also, if the user is prone to feeling nervous about a certain task, an intuitive instruction reception method for that task can be provided. Furthermore, the reception unit can prepare in advance a response based on the user's emotional patterns in accordance with the predicted emotions. This makes it possible to receive appropriate instructions based on the user's emotional patterns.

[0087] The reception unit can automatically provide related additional information based on the user's instructions. For example, if the user instructs "Create minutes," detailed information about the related meeting is displayed. Also, if the user instructs "Link with the sales support system," related customer information can be displayed. Furthermore, the reception unit can also display a history of related past instructions based on the user's instructions. This makes it possible to provide related information based on the user's instructions.

[0088] The reception unit monitors the user's current activity status and can receive instructions at an appropriate timing. For example, if the user is in a meeting, the reception of instructions can be suspended until the meeting ends. Also, if the user is traveling, the reception of instructions can be delayed until the user has completed traveling. Furthermore, the reception unit can adjust the method of receiving instructions according to the user's activity status. This allows for flexible instruction reception according to the user's activity status.

[0089] The analysis unit can estimate the user's emotions and adjust the level of detail of the analysis results based on the estimated user emotions. For example, if the user is relaxed, detailed analysis results can be provided. On the other hand, if the user is in a hurry, concise analysis results can be provided. Furthermore, the analysis unit can adjust the display method of the analysis results according to the user's emotions. This makes it possible to provide appropriate analysis results based on the user's emotions.

[0090] When analyzing instructions, the analysis unit can improve the accuracy of the analysis by referring to the user's past instruction history. For example, when analyzing instructions similar to those issued by the user in the past, the analysis unit refers to the past analysis results. It can also extract specific patterns from the user's past instruction history and optimize the analysis algorithm. Furthermore, the analysis unit can analyze predicted instruction content in advance based on the user's past instruction history. This enables highly accurate analysis by utilizing the past instruction history.

[0091] When analyzing instructions, the analysis unit can adjust the analysis algorithm based on the user's current project or area of ​​interest. For example, when analyzing instructions related to a project currently being carried out by the user, an algorithm specialized for the project can be applied. The analysis unit can also prioritize analysis of related information based on the user's area of ​​interest. Furthermore, the analysis unit can dynamically adjust the analysis algorithm according to the progress of the user's project. This enables appropriate analysis based on the user's current project or area of ​​interest.

[0092] The analysis unit can estimate the user's emotions and adjust the presentation method of the analysis results based on the estimated emotions. For example, if the user is tense, it can provide simple and easy-to-understand analysis results. If the user is relaxed, it can also provide detailed analysis results. Furthermore, the analysis unit can adjust the format of the analysis results according to the user's emotions. This makes it possible to present appropriate analysis results based on the user's emotions.

[0093] When analyzing instructions, the analysis unit can take into account the attribute information of the person submitting the instructions. For example, if the submitter is a senior manager, a detailed analysis will be performed to provide highly accurate results. On the other hand, if the submitter is a new employee, it can provide simple and easy-to-understand analysis results. Furthermore, the analysis unit can adjust the analysis algorithm according to the submitter's position and experience. This enables appropriate analysis based on the submitter's attribute information.

[0094] The processing flow of the second embodiment will be briefly explained below.

[0095] Step 1: The reception unit receives verbal instructions from the user. The verbal instructions from the user include voice commands and instructions in natural language. The reception unit may receive the user's instructions using voice recognition technology and may also analyze the instructions using natural language processing technology. For example, the reception unit may convert the voice command into text and analyze the text. Step 2: The analysis unit analyzes the instructions received by the reception unit and converts them into specific tasks. The analysis unit uses natural language processing technology and AI to analyze the instructions and generate specific work procedures. Step 3: The execution unit executes various tasks based on the instructions analyzed by the analysis unit. The execution unit executes tasks using RPA and can also execute tasks in cooperation with various systems. For example, it accesses a minutes-taking system and creates minutes. Step 4: The providing unit provides the results of the work performed by the executing unit to the user. The providing unit may provide the results of the work as a text report or graph, and may display the results on the user's device.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0100] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0101] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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).

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0116] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0117] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0132] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0133] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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).

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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).

[0153] 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.

[0154] 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."

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] [Explanation of symbols]

[0168] 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 verbal instructions from a user; an analysis unit that analyzes the instruction received by the reception unit and converts it into a specific task; an execution unit that executes various tasks based on the instructions analyzed by the analysis unit; a providing unit that provides a user with a result of the work executed by the execution unit. A system characterized by:

2. The execution unit: Links with the minutes-taking system, sales support system, approval system, and application form creation system to automatically perform various tasks The system of claim 1 .

3. The reception unit Estimating a user's emotion and adjusting a method for accepting verbal instructions based on the estimated user's emotion The system of claim 1 .

4. The reception unit Analyze the user's past instruction history and select the optimal reception method The system of claim 1 .

5. The reception unit When accepting verbal instructions, filter them based on the user's current projects and areas of interest The system of claim 1 .

6. The reception unit Estimate the user's emotions and determine the priority of instructions to be accepted based on the estimated user emotions. The system of claim 1 .

7. The reception unit When accepting verbal instructions, the system takes into account the user's geographical location information and prioritizes accepting highly relevant instructions. The system of claim 1 .

8. The reception unit When receiving verbal instructions, analyze the user's social media activity and receive related instructions. The system of claim 1 .

9. The analysis unit Inferring user emotion and adjusting instruction analysis method based on the estimated user emotion The system of claim 1 .

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