system

Abstract

The system according to this embodiment aims to facilitate knowledge management within an organization by generating a user-specific chatbot. [Solution] The system according to the embodiment comprises a reception unit, a generation unit, and a deployment unit. The reception unit receives information from the user. The generation unit analyzes the information received by the reception unit and generates a chatbot. The deployment unit deploys the chatbot generated by the generation unit within the organization.

Description

Technical Field

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Background Art

Prior Art Documents

Patent Documents

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

Means for Solving the Problems

[0007] The system according to this embodiment can generate a user-specific chatbot, making knowledge management within the organization easier. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The knowledge management system according to an embodiment of the present invention is a system that streamlines organizational knowledge management using generative AI. This knowledge management system can "extend specialized knowledge" by creating a user-specific chatbot using generative AI and training it with specific information. This allows users to work with the support of AI that is useful for their specialized tasks. Furthermore, the created dedicated chatbot can be deployed within the organization, enabling knowledge management throughout the entire organization. For example, a user trains the chatbot with specific information. In this process, the user inputs information related to their work. For example, they can train the chatbot with work procedures, manuals, or personal experiences. This allows the chatbot to extend the user's specialized knowledge. Next, the generative AI creates a user-specific chatbot based on the learned information. The generative AI analyzes the information input by the user and generates the optimal chatbot. For example, if a user inputs a work procedure manual, the chatbot can provide support for the work based on that manual. Furthermore, the created chatbot can be deployed within the organization. This allows other employees to use the chatbot, enabling knowledge management throughout the entire organization. For example, a chatbot created by one employee can be used by other employees to improve work efficiency. This system allows for easy creation and updating of personal AI, and by sharing it within the organization, knowledge management is realized. This can lead to improved organizational productivity, increased engagement, and sustainable growth. In this way, the knowledge management system streamlines organizational knowledge management and facilitates the expansion of specialized knowledge.

[0029] The knowledge management system according to this embodiment comprises a reception unit, a generation unit, and a deployment unit. The reception unit receives information from users. User information includes, but is not limited to, work procedures, manuals, and personal experiences. For example, the reception unit allows users to input work procedures. The reception unit also allows users to input manuals. Furthermore, the reception unit allows users to input personal experiences. For example, the reception unit allows users to input past project experience. The generation unit uses a generation AI to analyze the information received by the reception unit and generate a chatbot. For example, the generation AI analyzes the user's input information and generates an optimal chatbot. The generation AI analyzes the user's input information using, for example, a text generation AI (e.g., LLM). The generation unit can also generate a chatbot based on work procedures using the generation AI. For example, the generation unit generates a chatbot that provides support for work based on work procedures. The deployment unit deploys the chatbot generated by the generation unit within the organization. For example, the deployment unit deploys the generated chatbot so that other employees can use it. The deployment unit can, for example, deploy the chatbot to a specific department within an organization. The deployment unit can also deploy the chatbot to the entire organization. This allows the knowledge management system according to the embodiment to streamline knowledge management by generating user-specific chatbots and deploying them within the organization. Some or all of the above-described processes in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input user input information into the generation AI and have the generation AI generate an optimal chatbot. Some or all of the above-described processes in the deployment unit may be performed using, for example, AI, or without a generation AI. For example, the deployment unit can deploy the generated chatbot within the organization in an optimal manner using AI.

[0030] The reception desk receives information from users. This information includes, but is not limited to, work procedures, manuals, and personal experiences. For example, users can input work procedures, manuals, and personal experiences. The reception desk allows users to input past project experience. The reception desk provides an interface for users to input information, making it easy for them to do so. For example, information can be entered through web-based forms or mobile applications, allowing users to input information anytime, anywhere. Furthermore, the reception desk automatically categorizes the input information, organizing it into appropriate categories. For example, work procedures are categorized under business processes, manuals under documents, and personal experiences under knowledge bases. This ensures efficient subsequent processing. The reception desk also checks the quality of the input information, detecting missing or incorrect information. For example, it alerts users if necessary steps are missing from work procedures or if there are typos in manuals. This improves the accuracy and reliability of the input information.

[0031] The generation unit uses a generation AI to analyze information received by the reception unit and generate a chatbot. For example, the generation unit uses a generation AI to analyze user input information and generate an optimal chatbot. The generation AI uses, for example, a text generation AI (e.g., LLM) to analyze user input information. The generation unit can also use the generation AI to generate chatbots based on work procedure manuals. For example, the generation unit generates a chatbot that provides support for work based on work procedure manuals. The generation AI utilizes natural language processing technology to understand user input information and generate appropriate responses. For example, it analyzes each step of the work procedure manual and generates specific answers to user questions based on that. The generation AI can also learn from past project experience and manual content and generate responses based on that. As a result, the generated chatbot can provide accurate answers to a variety of user questions. Furthermore, the generation unit also has a function to evaluate the performance of the generated chatbot and make improvements as needed. For example, it monitors the accuracy of the generated chatbot's responses and user satisfaction and identifies areas for improvement. This ensures that the quality of the generated chatbot is continuously improved.

[0032] The Deployment Unit deploys the chatbots generated by the Generation Unit within the organization. For example, the Deployment Unit can make the generated chatbots available to other employees. The Deployment Unit can also deploy the chatbots to specific departments within the organization. Furthermore, the Deployment Unit can deploy the chatbots across the entire organization. The Deployment Unit provides the infrastructure for rapid and efficient deployment of the generated chatbots. For example, it can use a cloud-based platform to distribute the chatbots to each department within the organization. The Deployment Unit also has the capability to monitor chatbot usage and perform updates and maintenance as needed. For example, it can analyze chatbot usage frequency and user feedback to identify areas for improvement. This ensures continuous improvement in chatbot performance and increased user satisfaction. In addition, the Deployment Unit can provide training and support regarding chatbot usage. For example, it can hold training sessions for employees to teach them how to use the chatbot effectively. It can also set up a support desk to handle questions and problems related to the chatbot. This ensures that employees can get the most out of the chatbot.

[0033] The reception area allows users to input information such as work procedures, manuals, and personal experiences. For example, users can input work procedures. The reception area can also input manuals. Furthermore, users can input personal experiences. For example, users can input past project experience. This allows users to input work-related information, providing the information necessary for generating a chatbot. Some or all of the above processing in the reception area may be performed using AI, or not. For example, the reception area can analyze the information entered by the user using AI and extract the most relevant information.

[0034] The generation unit can analyze user input information using a generation AI and generate an optimal chatbot. For example, the generation unit uses a generation AI to analyze user input information and generate an optimal chatbot. The generation AI analyzes user input information using, for example, a text generation AI (e.g., LLM). The generation unit can also use the generation AI to generate a chatbot based on business procedure manuals. For example, the generation unit generates a chatbot that provides business support based on business procedure manuals. In this way, by using a generation AI, an optimal chatbot can be generated based on user input information. Some or all of the above-described processes in the generation unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the generation unit can input user input information into the generation AI and have the generation AI execute the generation of an optimal chatbot.

[0035] The deployment unit can deploy the generated chatbot so that other employees within the organization can use it. For example, the deployment unit can deploy the generated chatbot so that other employees can use it. For example, the deployment unit can deploy the chatbot to a specific department within the organization. The deployment unit can also deploy the chatbot to the entire organization. This makes the generated chatbot available to other employees by deploying it within the organization. Some or all of the above processes in the deployment unit may be performed using AI, for example, or not using AI. For example, the deployment unit can use AI to deploy the generated chatbot within the organization in the most optimal way.

[0036] The generation unit enables a chatbot to provide support for business operations based on business procedure manuals, using a generation AI. For example, the generation unit generates a chatbot that provides support for business operations based on business procedure manuals. The generation AI can analyze business procedure manuals using a text generation AI (e.g., LLM) and generate a chatbot that provides support for business operations. The generation unit can also generate a chatbot based on business procedure manuals using a generation AI. For example, the generation unit generates a chatbot that provides support for business operations based on business procedure manuals. This improves business efficiency by enabling the chatbot to provide support for business operations based on business procedure manuals. Some or all of the above-described processes in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input business procedure manuals into a generation AI and have the generation AI generate a chatbot that provides support for business operations.

[0037] The deployment unit can improve work efficiency by allowing other employees to use it. For example, the deployment unit can improve work efficiency by allowing other employees to use the generated chatbot. For example, the deployment unit can improve work efficiency by deploying the chatbot to a specific department within the organization and allowing employees in that department to use it. Furthermore, the deployment unit can improve work efficiency by deploying the chatbot throughout the entire organization and allowing all employees to use it. This allows other employees to improve work efficiency by using the chatbot. Some or all of the above processing in the deployment unit may be performed using AI, for example, or without AI. For example, the deployment unit can improve work efficiency by deploying the generated chatbot within the organization in the most optimal way using AI and allowing other employees to use it.

[0038] The reception desk can analyze the user's past information input history and select the optimal reception method. For example, the reception desk may prioritize suggesting input methods that the user has frequently used in the past. For example, the reception desk may select the most efficient input method from the user's past input history. The reception desk can also analyze patterns in the information the user has entered in the past and suggest the optimal reception method. In this way, by analyzing past information input history, the reception desk can provide the user with the most suitable reception method. 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 input history into AI and have the AI ​​select the optimal reception method.

[0039] The reception unit can filter information upon receipt based on the user's current projects and areas of interest. For example, the reception unit may only accept information related to the user's current project. For example, the reception unit may prioritize receiving highly relevant information based on the user's areas of interest. The reception unit can also filter and accept necessary information according to the user's project progress. This allows the reception unit to receive highly relevant information by filtering information based on the user's projects and areas of interest. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's project information into an AI and have the AI ​​perform the filtering of highly relevant information.

[0040] The reception unit can prioritize receiving highly relevant information by considering the user's geographical location when receiving information. For example, if the user is in a specific region, the reception unit will prioritize receiving information related to that region. For example, if the user is on the move, the reception unit will receive necessary information based on the user's current location. The reception unit can also prioritize receiving information related to a specific location if the user is in that location. In this way, by considering the user's geographical location, highly relevant information can be prioritized. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's geographical location information into the AI ​​and have the AI ​​perform the priority receiving of highly relevant information.

[0041] The reception unit can analyze the user's social media activity when receiving information and receive relevant information. For example, the reception unit can receive relevant information based on information shared by the user on social media. For example, the reception unit can prioritize receiving information shared by the user's social media followers and friends. The reception unit can also analyze the user's social media activity history and receive highly relevant information. In this way, highly relevant information can be received by analyzing the user's social media activity. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's social media activity data into AI and have the AI ​​perform the reception of relevant information.

[0042] The generation unit can adjust the level of detail of the generated chatbot based on the importance of the input information. For example, the generation unit can generate a chatbot with a detailed explanation for information of high importance. For example, the generation unit can generate a chatbot with a concise explanation for information of low importance. The generation unit can also generate a chatbot with an appropriate level of detail for information of moderate importance. In this way, by adjusting the level of detail of the generated chatbot based on the importance of the information, a chatbot with the appropriate level of detail can be generated. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the input information into a generation AI and have the generation AI perform the adjustment of the level of detail of the generated chatbot based on the importance of the information.

[0043] The generation unit can apply different generation algorithms depending on the category of information when generating a chatbot. For example, the generation unit can apply a specialized algorithm to technical information to generate a chatbot. For example, the generation unit can apply a general-purpose algorithm to general information to generate a chatbot. Furthermore, the generation unit can apply an industry-specific algorithm to information related to a particular industry to generate a chatbot. In this way, by applying different generation algorithms depending on the category of information, the optimal chatbot can be generated. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the category of information into the generation AI and cause the generation AI to execute the application of a generation algorithm according to the category.

[0044] The generation unit can determine the generation priority based on the information submission date when generating chatbots. For example, the generation unit can prioritize generating chatbots for recently submitted information. For example, it can postpone generating chatbots for older information. The generation unit can also generate chatbots with a moderate priority for information that has been submitted for a moderate period of time. In this way, by determining the generation priority based on the information submission date, chatbots can be generated in an appropriate order. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the information submission date into the generation AI and have the generation AI determine the generation priority.

[0045] The generation unit can adjust the generation order based on the relevance of the information when generating chatbots. For example, the generation unit can prioritize generating chatbots for highly relevant information. For example, it can postpone generating chatbots for less relevant information. The generation unit can also generate chatbots for moderately relevant information in an appropriate order. By adjusting the generation order based on the relevance of the information, highly relevant information can be prioritized and reflected in the chatbot. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the relevance of the information into the generation AI and have the generation AI adjust the generation order.

[0046] The deployment unit can select the optimal deployment method by referring to past deployment data when deploying a chatbot. For example, the deployment unit can select the optimal deployment method based on past successful deployment methods. For example, the deployment unit can analyze past deployment data and select the most effective deployment method. The deployment unit can also avoid failed deployment methods from past deployment data. In this way, the optimal deployment method can be selected by referring to past deployment data. Some or all of the above processing in the deployment unit may be performed using AI, for example, or without AI. For example, the deployment unit can input past deployment data into AI and have the AI ​​perform the selection of the optimal deployment method.

[0047] The deployment unit can customize the deployment method when deploying chatbots according to specific departments or projects within the organization. For example, the deployment unit can deploy a chatbot tailored to a specific department. For example, the deployment unit can deploy a chatbot that provides information related to a specific project. The deployment unit can also deploy a general-purpose chatbot to the entire organization. This allows for customized deployment methods tailored to specific departments or projects. Some or all of the above processes in the deployment unit may be performed using AI, for example, or not. For example, the deployment unit can input information about specific departments or projects into the AI ​​and have the AI ​​perform the customization of the deployment method.

[0048] The deployment unit can select the optimal deployment method when deploying chatbots, taking into account the geographical distribution within the organization. For example, the deployment unit can deploy a chatbot tailored to a specific region within the organization. For example, the deployment unit can deploy customized chatbots for each region across multiple regions within the organization. The deployment unit can also deploy a general-purpose chatbot for the entire organization. This allows for the selection of the optimal deployment method by considering the geographical distribution within the organization. Some or all of the above processing in the deployment unit may be performed using AI, for example, or without AI. For example, the deployment unit can input geographical distribution data within the organization into AI and have the AI ​​select the optimal deployment method.

[0049] The deployment unit can improve the accuracy of the chatbot deployment by referencing relevant projects within the organization. For example, the deployment unit can deploy a chatbot that provides information related to a specific project. For example, the deployment unit can deploy a customized chatbot for each project across multiple projects within the organization. The deployment unit can also deploy a general-purpose chatbot for the entire organization. This allows for improved deployment accuracy by referencing relevant projects. Some or all of the above processing in the deployment unit may be performed using AI, for example, or not. For example, the deployment unit can input relevant project data within the organization into the AI ​​and have the AI ​​perform the task of improving deployment accuracy.

[0050] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0051] The knowledge management system may also include a feedback unit. The feedback unit can collect user feedback and use it to improve the generated chatbot. For example, the feedback unit can provide a function for users to evaluate the chatbot's responses. By evaluating whether users are satisfied with the chatbot's responses, the generation unit can improve the accuracy of the chatbot's responses based on that evaluation. The feedback unit can also provide a function for users to suggest specific improvements to the chatbot. This allows the generation unit to collect specific user feedback and improve the chatbot's functionality based on that feedback. Furthermore, the feedback unit can also provide a function to analyze user feedback and identify common problems. This allows the generation unit to take corrective measures to solve these common problems. Some or all of the above processing in the feedback unit may be performed using AI, for example, or not. For example, the feedback unit can input user feedback data into AI and have the AI ​​perform the feedback analysis.

[0052] The knowledge management system may also include a notification unit. This unit can notify users of updates to the generated chatbot and the addition of new features. For example, the notification unit can notify users when a new version of the chatbot is released, ensuring users always have access to the latest chatbot. The notification unit can also notify users of how to use new features added to the chatbot, enabling them to effectively utilize the new features. Furthermore, the notification unit can provide timely notifications to users based on their chatbot usage. For example, if a user frequently uses the chatbot, notifications can be sent at appropriate times based on their usage. Some or all of the above-described processes in the notification unit may be performed using AI, or not. For example, the notification unit can input user usage data into the AI ​​and have the AI ​​execute the notification timing.

[0053] The knowledge management system may also include a recommendation section. This recommendation section can recommend the most suitable chatbots and information based on the user's past usage history. For example, it might prioritize recommending chatbots that the user has frequently used in the past. This allows the user to easily find the chatbot that is most helpful to them. The recommendation section can also recommend highly relevant information based on the user's past usage history. For example, if a user frequently uses information related to a specific project, it can recommend new information related to that project. Furthermore, the recommendation section can analyze the user's usage patterns and suggest the optimal way to use the system. This allows the user to use the chatbot efficiently. Some or all of the above-described processes in the recommendation section may be performed using AI, for example, or not. For example, the recommendation section could input user usage history data into an AI and have the AI ​​make optimal recommendations.

[0054] The knowledge management system may also include a monitoring unit. The monitoring unit can monitor the chatbot's usage in real time and detect anomalies. For example, the monitoring unit can detect an anomaly if the chatbot's response time is unusually long. This allows the system administrator to respond quickly. The monitoring unit can also detect an anomaly if the chatbot's usage frequency increases sharply. This allows for proper management of the system load. Furthermore, the monitoring unit can analyze the chatbot's usage and detect changes in usage patterns. This allows the system administrator to take appropriate action according to the usage situation. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input chatbot usage data into AI and have the AI ​​perform anomaly detection.

[0055] The knowledge management system may also include a training section. The training section can provide training to help users learn how to use the chatbot. For example, the training section could provide tutorials explaining the basic usage of the chatbot. This would allow users to acquire the basic knowledge necessary to effectively use the chatbot. The training section could also provide training programs tailored to the user's skill level. For example, it could offer a wide range of training programs, from basic training for beginners to advanced training for experienced users. Furthermore, the training section could manage the user's training history and provide feedback based on their progress. This would allow users to receive appropriate training according to their skill level. Some or all of the above processes in the training section may be performed using AI, or not. For example, the training section could input the user's training history data into an AI and have the AI ​​provide the training program.

[0056] The following briefly describes the processing flow for example form 1.

[0057] Step 1: The reception desk receives information from users. This information includes work procedures, manuals, and personal experience. For example, users can input work procedures, manuals, and past project experience. Step 2: The generation unit uses a generation AI to analyze the information received by the reception unit and generate a chatbot. The generation AI, for example, uses a text generation AI (e.g., LLM) to analyze the user's input information and generate the optimal chatbot. The generation unit can also generate a chatbot that provides support for business operations based on business procedure manuals. Step 3: The deployment unit deploys the chatbot generated by the generation unit within the organization. The deployment unit can deploy the generated chatbot so that other employees can use it, and it can be deployed to specific departments or the entire organization.

[0058] (Example of form 2) The knowledge management system according to an embodiment of the present invention is a system that streamlines organizational knowledge management using generative AI. This knowledge management system can "extend specialized knowledge" by creating a user-specific chatbot using generative AI and training it with specific information. This allows users to work with the support of AI that is useful for their specialized tasks. Furthermore, the created dedicated chatbot can be deployed within the organization, enabling knowledge management throughout the entire organization. For example, a user trains the chatbot with specific information. In this process, the user inputs information related to their work. For example, they can train the chatbot with work procedures, manuals, or personal experiences. This allows the chatbot to extend the user's specialized knowledge. Next, the generative AI creates a user-specific chatbot based on the learned information. The generative AI analyzes the information input by the user and generates the optimal chatbot. For example, if a user inputs a work procedure manual, the chatbot can provide support for the work based on that manual. Furthermore, the created chatbot can be deployed within the organization. This allows other employees to use the chatbot, enabling knowledge management throughout the entire organization. For example, a chatbot created by one employee can be used by other employees to improve work efficiency. This system allows for easy creation and updating of personal AI, and by sharing it within the organization, knowledge management is realized. This can lead to improved organizational productivity, increased engagement, and sustainable growth. In this way, the knowledge management system streamlines organizational knowledge management and facilitates the expansion of specialized knowledge.

[0059] The knowledge management system according to this embodiment comprises a reception unit, a generation unit, and a deployment unit. The reception unit receives information from users. User information includes, but is not limited to, work procedures, manuals, and personal experiences. For example, the reception unit allows users to input work procedures. The reception unit also allows users to input manuals. Furthermore, the reception unit allows users to input personal experiences. For example, the reception unit allows users to input past project experience. The generation unit uses a generation AI to analyze the information received by the reception unit and generate a chatbot. For example, the generation AI analyzes the user's input information and generates an optimal chatbot. The generation AI analyzes the user's input information using, for example, a text generation AI (e.g., LLM). The generation unit can also generate a chatbot based on work procedures using the generation AI. For example, the generation unit generates a chatbot that provides support for work based on work procedures. The deployment unit deploys the chatbot generated by the generation unit within the organization. For example, the deployment unit deploys the generated chatbot so that other employees can use it. The deployment unit can, for example, deploy the chatbot to a specific department within an organization. The deployment unit can also deploy the chatbot to the entire organization. This allows the knowledge management system according to the embodiment to streamline knowledge management by generating user-specific chatbots and deploying them within the organization. Some or all of the above-described processes in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input user input information into the generation AI and have the generation AI generate an optimal chatbot. Some or all of the above-described processes in the deployment unit may be performed using, for example, AI, or without a generation AI. For example, the deployment unit can deploy the generated chatbot within the organization in an optimal manner using AI.

[0060] The reception desk receives information from users. This information includes, but is not limited to, work procedures, manuals, and personal experiences. For example, users can input work procedures, manuals, and personal experiences. The reception desk allows users to input past project experience. The reception desk provides an interface for users to input information, making it easy for them to do so. For example, information can be entered through web-based forms or mobile applications, allowing users to input information anytime, anywhere. Furthermore, the reception desk automatically categorizes the input information, organizing it into appropriate categories. For example, work procedures are categorized under business processes, manuals under documents, and personal experiences under knowledge bases. This ensures efficient subsequent processing. The reception desk also checks the quality of the input information, detecting missing or incorrect information. For example, it alerts users if necessary steps are missing from work procedures or if there are typos in manuals. This improves the accuracy and reliability of the input information.

[0061] The generation unit uses a generation AI to analyze information received by the reception unit and generate a chatbot. For example, the generation unit uses a generation AI to analyze user input information and generate an optimal chatbot. The generation AI uses, for example, a text generation AI (e.g., LLM) to analyze user input information. The generation unit can also use the generation AI to generate chatbots based on work procedure manuals. For example, the generation unit generates a chatbot that provides support for work based on work procedure manuals. The generation AI utilizes natural language processing technology to understand user input information and generate appropriate responses. For example, it analyzes each step of the work procedure manual and generates specific answers to user questions based on that. The generation AI can also learn from past project experience and manual content and generate responses based on that. As a result, the generated chatbot can provide accurate answers to a variety of user questions. Furthermore, the generation unit also has a function to evaluate the performance of the generated chatbot and make improvements as needed. For example, it monitors the accuracy of the generated chatbot's responses and user satisfaction and identifies areas for improvement. This ensures that the quality of the generated chatbot is continuously improved.

[0062] The Deployment Unit deploys the chatbots generated by the Generation Unit within the organization. For example, the Deployment Unit can make the generated chatbots available to other employees. The Deployment Unit can also deploy the chatbots to specific departments within the organization. Furthermore, the Deployment Unit can deploy the chatbots across the entire organization. The Deployment Unit provides the infrastructure for rapid and efficient deployment of the generated chatbots. For example, it can use a cloud-based platform to distribute the chatbots to each department within the organization. The Deployment Unit also has the capability to monitor chatbot usage and perform updates and maintenance as needed. For example, it can analyze chatbot usage frequency and user feedback to identify areas for improvement. This ensures continuous improvement in chatbot performance and increased user satisfaction. In addition, the Deployment Unit can provide training and support regarding chatbot usage. For example, it can hold training sessions for employees to teach them how to use the chatbot effectively. It can also set up a support desk to handle questions and problems related to the chatbot. This ensures that employees can get the most out of the chatbot.

[0063] The reception area allows users to input information such as work procedures, manuals, and personal experiences. For example, users can input work procedures. The reception area can also input manuals. Furthermore, users can input personal experiences. For example, users can input past project experience. This allows users to input work-related information, providing the information necessary for generating a chatbot. Some or all of the above processing in the reception area may be performed using AI, or not. For example, the reception area can analyze the information entered by the user using AI and extract the most relevant information.

[0064] The generation unit can analyze user input information using a generation AI and generate an optimal chatbot. For example, the generation unit uses a generation AI to analyze user input information and generate an optimal chatbot. The generation AI analyzes user input information using, for example, a text generation AI (e.g., LLM). The generation unit can also use the generation AI to generate a chatbot based on business procedure manuals. For example, the generation unit generates a chatbot that provides business support based on business procedure manuals. In this way, by using a generation AI, an optimal chatbot can be generated based on user input information. Some or all of the above-described processes in the generation unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the generation unit can input user input information into the generation AI and have the generation AI execute the generation of an optimal chatbot.

[0065] The deployment unit can deploy the generated chatbot so that other employees within the organization can use it. For example, the deployment unit can deploy the generated chatbot so that other employees can use it. For example, the deployment unit can deploy the chatbot to a specific department within the organization. The deployment unit can also deploy the chatbot to the entire organization. This makes the generated chatbot available to other employees by deploying it within the organization. Some or all of the above processes in the deployment unit may be performed using AI, for example, or not using AI. For example, the deployment unit can use AI to deploy the generated chatbot within the organization in the most optimal way.

[0066] The generation unit enables a chatbot to provide support for business operations based on business procedure manuals, using a generation AI. For example, the generation unit generates a chatbot that provides support for business operations based on business procedure manuals. The generation AI can analyze business procedure manuals using a text generation AI (e.g., LLM) and generate a chatbot that provides support for business operations. The generation unit can also generate a chatbot based on business procedure manuals using a generation AI. For example, the generation unit generates a chatbot that provides support for business operations based on business procedure manuals. This improves business efficiency by enabling the chatbot to provide support for business operations based on business procedure manuals. Some or all of the above-described processes in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input business procedure manuals into a generation AI and have the generation AI generate a chatbot that provides support for business operations.

[0067] The deployment unit can improve work efficiency by allowing other employees to use it. For example, the deployment unit can improve work efficiency by allowing other employees to use the generated chatbot. For example, the deployment unit can improve work efficiency by deploying the chatbot to a specific department within the organization and allowing employees in that department to use it. Furthermore, the deployment unit can improve work efficiency by deploying the chatbot throughout the entire organization and allowing all employees to use it. This allows other employees to improve work efficiency by using the chatbot. Some or all of the above processing in the deployment unit may be performed using AI, for example, or without AI. For example, the deployment unit can improve work efficiency by deploying the generated chatbot within the organization in the most optimal way using AI and allowing other employees to use it.

[0068] The reception unit can estimate the user's emotions and adjust the timing of information reception based on the estimated emotions. For example, if the user is stressed, the reception unit will receive information at a time when the user can relax. For example, if the user is concentrating, the reception unit will receive information in a way that does not interrupt their concentration. Also, if the user is tired, the reception unit can receive information after they have taken a break. In this way, by adjusting the timing of information reception according to the user's emotions, information can be received at a more appropriate time. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception unit may be performed using AI, or not using AI. For example, the reception unit can input user emotion data into AI and have the AI ​​perform emotion estimation.

[0069] The reception desk can analyze the user's past information input history and select the optimal reception method. For example, the reception desk may prioritize suggesting input methods that the user has frequently used in the past. For example, the reception desk may select the most efficient input method from the user's past input history. The reception desk can also analyze patterns in the information the user has entered in the past and suggest the optimal reception method. In this way, by analyzing past information input history, the reception desk can provide the user with the most suitable reception method. 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 input history into AI and have the AI ​​select the optimal reception method.

[0070] The reception unit can filter information upon receipt based on the user's current projects and areas of interest. For example, the reception unit may only accept information related to the user's current project. For example, the reception unit may prioritize receiving highly relevant information based on the user's areas of interest. The reception unit can also filter and accept necessary information according to the user's project progress. This allows the reception unit to receive highly relevant information by filtering information based on the user's projects and areas of interest. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's project information into an AI and have the AI ​​perform the filtering of highly relevant information.

[0071] The reception desk can estimate the user's emotions and determine the priority of information to receive based on the estimated emotions. For example, if the user is stressed, the reception desk will postpone receiving less important information. If the user is relaxed, the reception desk will prioritize receiving more important information. The reception desk can also prioritize receiving urgent information if the user is in a hurry. In this way, important information can be received preferentially by determining the priority of information according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI, or not using AI. For example, the reception desk can input user emotion data into an AI and have the AI ​​perform the determination of information priority.

[0072] The reception unit can prioritize receiving highly relevant information by considering the user's geographical location when receiving information. For example, if the user is in a specific region, the reception unit will prioritize receiving information related to that region. For example, if the user is on the move, the reception unit will receive necessary information based on the user's current location. The reception unit can also prioritize receiving information related to a specific location if the user is in that location. In this way, by considering the user's geographical location, highly relevant information can be prioritized. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's geographical location information into the AI ​​and have the AI ​​perform the priority receiving of highly relevant information.

[0073] The reception unit can analyze the user's social media activity when receiving information and receive relevant information. For example, the reception unit can receive relevant information based on information shared by the user on social media. For example, the reception unit can prioritize receiving information shared by the user's social media followers and friends. The reception unit can also analyze the user's social media activity history and receive highly relevant information. In this way, highly relevant information can be received by analyzing the user's social media activity. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's social media activity data into AI and have the AI ​​perform the reception of relevant information.

[0074] The generation unit can estimate the user's emotions and adjust the chatbot generation method based on the estimated emotions. For example, if the user is relaxed, the generation unit can generate a chatbot that proceeds at a relaxed pace. If the user is in a hurry, the generation unit can generate a chatbot that provides information quickly. The generation unit can also generate a chatbot with visually stimulating effects if the user is excited. This allows for the generation of a more appropriate chatbot by adjusting the chatbot generation method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the generation unit may be performed using a generative AI or not. For example, the generation unit can input user emotion data into a generative AI and have the generative AI adjust the chatbot generation method.

[0075] The generation unit can adjust the level of detail of the generated chatbot based on the importance of the input information. For example, the generation unit can generate a chatbot with a detailed explanation for information of high importance. For example, the generation unit can generate a chatbot with a concise explanation for information of low importance. The generation unit can also generate a chatbot with an appropriate level of detail for information of moderate importance. In this way, by adjusting the level of detail of the generated chatbot based on the importance of the information, a chatbot with the appropriate level of detail can be generated. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the input information into a generation AI and have the generation AI perform the adjustment of the level of detail of the generated chatbot based on the importance of the information.

[0076] The generation unit can apply different generation algorithms depending on the category of information when generating a chatbot. For example, the generation unit can apply a specialized algorithm to technical information to generate a chatbot. For example, the generation unit can apply a general-purpose algorithm to general information to generate a chatbot. Furthermore, the generation unit can apply an industry-specific algorithm to information related to a particular industry to generate a chatbot. In this way, by applying different generation algorithms depending on the category of information, the optimal chatbot can be generated. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the category of information into the generation AI and cause the generation AI to execute the application of a generation algorithm according to the category.

[0077] The generation unit can estimate the user's emotions and adjust the functions of the generated chatbot based on the estimated emotions. For example, if the user is relaxed, the generation unit will generate a chatbot with a relaxed atmosphere. If the user is in a hurry, the generation unit will generate a chatbot that provides information quickly. The generation unit can also generate a chatbot with visually stimulating effects if the user is excited. In this way, a more appropriate chatbot can be generated by adjusting the chatbot's functions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input user emotion data into a generation AI and have the generation AI adjust the chatbot's functions.

[0078] The generation unit can determine the generation priority based on the information submission date when generating chatbots. For example, the generation unit can prioritize generating chatbots for recently submitted information. For example, it can postpone generating chatbots for older information. The generation unit can also generate chatbots with a moderate priority for information that has been submitted for a moderate period of time. In this way, by determining the generation priority based on the information submission date, chatbots can be generated in an appropriate order. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the information submission date into the generation AI and have the generation AI determine the generation priority.

[0079] The generation unit can adjust the generation order based on the relevance of the information when generating chatbots. For example, the generation unit can prioritize generating chatbots for highly relevant information. For example, it can postpone generating chatbots for less relevant information. The generation unit can also generate chatbots for moderately relevant information in an appropriate order. By adjusting the generation order based on the relevance of the information, highly relevant information can be prioritized and reflected in the chatbot. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the relevance of the information into the generation AI and have the generation AI adjust the generation order.

[0080] The deployment unit can estimate the user's emotions and adjust the chatbot's deployment method based on the estimated emotions. For example, if the user is relaxed, the deployment unit will deploy a chatbot that proceeds at a relaxed pace. If the user is in a hurry, the deployment unit will deploy a chatbot that provides information quickly. The deployment unit can also deploy a chatbot with visually stimulating effects if the user is excited. This allows for a more appropriate deployment by adjusting the chatbot's deployment method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the deployment unit may be performed using AI or not using AI. For example, the deployment unit can input user emotion data into AI and have the AI ​​adjust the chatbot's deployment method.

[0081] The deployment unit can select the optimal deployment method by referring to past deployment data when deploying a chatbot. For example, the deployment unit can select the optimal deployment method based on past successful deployment methods. For example, the deployment unit can analyze past deployment data and select the most effective deployment method. The deployment unit can also avoid failed deployment methods from past deployment data. In this way, the optimal deployment method can be selected by referring to past deployment data. Some or all of the above processing in the deployment unit may be performed using AI, for example, or without AI. For example, the deployment unit can input past deployment data into AI and have the AI ​​perform the selection of the optimal deployment method.

[0082] The deployment unit can customize the deployment method when deploying chatbots according to specific departments or projects within the organization. For example, the deployment unit can deploy a chatbot tailored to a specific department. For example, the deployment unit can deploy a chatbot that provides information related to a specific project. The deployment unit can also deploy a general-purpose chatbot to the entire organization. This allows for customized deployment methods tailored to specific departments or projects. Some or all of the above processes in the deployment unit may be performed using AI, for example, or not. For example, the deployment unit can input information about specific departments or projects into the AI ​​and have the AI ​​perform the customization of the deployment method.

[0083] The deployment unit can estimate the user's emotions and adjust the deployment order of the chatbots based on the estimated emotions. For example, if the user is relaxed, the deployment unit will deploy a chatbot that proceeds at a relaxed pace. If the user is in a hurry, the deployment unit will deploy a chatbot that provides information quickly. The deployment unit can also deploy a chatbot with visually stimulating effects if the user is excited. By adjusting the deployment order of the chatbots according to the user's emotions, a more appropriate deployment order can be achieved. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the deployment unit may be performed using AI or not using AI. For example, the deployment unit can input user emotion data into AI and have the AI ​​adjust the deployment order of the chatbots.

[0084] The deployment unit can select the optimal deployment method when deploying chatbots, taking into account the geographical distribution within the organization. For example, the deployment unit can deploy a chatbot tailored to a specific region within the organization. For example, the deployment unit can deploy customized chatbots for each region across multiple regions within the organization. The deployment unit can also deploy a general-purpose chatbot for the entire organization. This allows for the selection of the optimal deployment method by considering the geographical distribution within the organization. Some or all of the above processing in the deployment unit may be performed using AI, for example, or without AI. For example, the deployment unit can input geographical distribution data within the organization into AI and have the AI ​​select the optimal deployment method.

[0085] The deployment unit can improve the accuracy of the chatbot deployment by referencing relevant projects within the organization. For example, the deployment unit can deploy a chatbot that provides information related to a specific project. For example, the deployment unit can deploy a customized chatbot for each project across multiple projects within the organization. The deployment unit can also deploy a general-purpose chatbot for the entire organization. This allows for improved deployment accuracy by referencing relevant projects. Some or all of the above processing in the deployment unit may be performed using AI, for example, or not. For example, the deployment unit can input relevant project data within the organization into the AI ​​and have the AI ​​perform the task of improving deployment accuracy.

[0086] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0087] The knowledge management system may also include a feedback unit. The feedback unit can collect user feedback and use it to improve the generated chatbot. For example, the feedback unit can provide a function for users to evaluate the chatbot's responses. By evaluating whether users are satisfied with the chatbot's responses, the generation unit can improve the accuracy of the chatbot's responses based on that evaluation. The feedback unit can also provide a function for users to suggest specific improvements to the chatbot. This allows the generation unit to collect specific user feedback and improve the chatbot's functionality based on that feedback. Furthermore, the feedback unit can also provide a function to analyze user feedback and identify common problems. This allows the generation unit to take corrective measures to solve these common problems. Some or all of the above processing in the feedback unit may be performed using AI, for example, or not. For example, the feedback unit can input user feedback data into AI and have the AI ​​perform the feedback analysis.

[0088] The knowledge management system may also include a notification unit. This unit can notify users of updates to the generated chatbot and the addition of new features. For example, the notification unit can notify users when a new version of the chatbot is released, ensuring users always have access to the latest chatbot. The notification unit can also notify users of how to use new features added to the chatbot, enabling them to effectively utilize the new features. Furthermore, the notification unit can provide timely notifications to users based on their chatbot usage. For example, if a user frequently uses the chatbot, notifications can be sent at appropriate times based on their usage. Some or all of the above-described processes in the notification unit may be performed using AI, or not. For example, the notification unit can input user usage data into the AI ​​and have the AI ​​execute the notification timing.

[0089] The knowledge management system may also include a recommendation section. This recommendation section can recommend the most suitable chatbots and information based on the user's past usage history. For example, it might prioritize recommending chatbots that the user has frequently used in the past. This allows the user to easily find the chatbot that is most helpful to them. The recommendation section can also recommend highly relevant information based on the user's past usage history. For example, if a user frequently uses information related to a specific project, it can recommend new information related to that project. Furthermore, the recommendation section can analyze the user's usage patterns and suggest the optimal way to use the system. This allows the user to use the chatbot efficiently. Some or all of the above-described processes in the recommendation section may be performed using AI, for example, or not. For example, the recommendation section could input user usage history data into an AI and have the AI ​​make optimal recommendations.

[0090] The knowledge management system may also include a monitoring unit. The monitoring unit can monitor the chatbot's usage in real time and detect anomalies. For example, the monitoring unit can detect an anomaly if the chatbot's response time is unusually long. This allows the system administrator to respond quickly. The monitoring unit can also detect an anomaly if the chatbot's usage frequency increases sharply. This allows for proper management of the system load. Furthermore, the monitoring unit can analyze the chatbot's usage and detect changes in usage patterns. This allows the system administrator to take appropriate action according to the usage situation. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input chatbot usage data into AI and have the AI ​​perform anomaly detection.

[0091] The knowledge management system may also include a training section. The training section can provide training to help users learn how to use the chatbot. For example, the training section could provide tutorials explaining the basic usage of the chatbot. This would allow users to acquire the basic knowledge necessary to effectively use the chatbot. The training section could also provide training programs tailored to the user's skill level. For example, it could offer a wide range of training programs, from basic training for beginners to advanced training for experienced users. Furthermore, the training section could manage the user's training history and provide feedback based on their progress. This would allow users to receive appropriate training according to their skill level. Some or all of the above processes in the training section may be performed using AI, or not. For example, the training section could input the user's training history data into an AI and have the AI ​​provide the training program.

[0092] The knowledge management system may also include an emotion analysis unit. This unit can analyze the user's emotions in real time and adjust the chatbot's response based on the results. For example, if the user is stressed, the emotion analysis unit can provide a relaxing response, allowing the user to use the chatbot while reducing stress. Similarly, if the user is agitated, the emotion analysis unit can provide a response to alleviate that agitation, allowing the user to use the chatbot calmly. Furthermore, the emotion analysis unit can continuously monitor changes in the user's emotions and adjust the response at the appropriate time, providing the optimal response tailored to the user's emotions. Some or all of the above processing in the emotion analysis unit may be performed using AI, or without AI. For example, the emotion analysis unit can input user emotion data into an AI and have the AI ​​perform the emotion analysis.

[0093] The knowledge management system may further include an emotional feedback unit. The emotional feedback unit can evaluate the chatbot's responses based on the user's emotions and suggest improvements. For example, if the user is dissatisfied with the chatbot's response, the emotional feedback unit can identify the cause of the dissatisfaction and suggest improvements. This allows the generation unit to improve the chatbot's responses based on these suggestions. Furthermore, if the user is satisfied with the chatbot's response, the emotional feedback unit can evaluate that response and reflect it in other responses. This improves the overall response quality of the chatbot. Additionally, the emotional feedback unit can analyze the user's emotional data and identify common problems. This allows the generation unit to implement improvements to address these common problems. Some or all of the above processing in the emotional feedback unit may be performed using AI, or not. For example, the emotional feedback unit can input the user's emotional data into AI and have the AI ​​perform the feedback analysis.

[0094] The knowledge management system may further include an emotion estimation unit. The emotion estimation unit can estimate the user's emotions and adjust the chatbot's response based on those emotions. For example, if the user is sad, the emotion estimation unit can offer words of encouragement. This allows the user to use the chatbot while feeling better. The emotion estimation unit can also provide a response that shares the user's joy if the user is happy. This allows the user to use the chatbot in a positive mood. Furthermore, the emotion estimation unit can continuously monitor changes in the user's emotions and adjust the response at the appropriate time. This allows for the provision of the optimal response according to the user's emotions. Some or all of the above processing in the emotion estimation unit may be performed using AI, for example, or not using AI. For example, the emotion estimation unit can input the user's emotion data into AI and have the AI ​​perform emotion estimation.

[0095] The knowledge management system may also include an emotion-adaptive unit. This unit can adjust the chatbot interface based on the user's emotions. For example, if the user is stressed, the unit can change the interface's colors and layout to a more relaxing one. This allows the user to use the chatbot while reducing stress. The unit can also provide an interface that soothes the user's excitement if they are agitated, allowing them to use the chatbot calmly. Furthermore, the unit can continuously monitor changes in the user's emotions and adjust the interface at the appropriate time. This provides an optimal interface tailored to the user's emotions. Some or all of the above processing in the emotion-adaptive unit may be performed using AI, for example, or without AI. For example, the emotion-adaptive unit can input user emotion data into AI and have the AI ​​perform the interface adjustments.

[0096] The knowledge management system may also include an emotion history unit. This unit can record the user's emotion history and optimize the chatbot's responses based on that history. For example, the emotion history unit can record what emotions the user has felt in the past and adjust the current response based on that history. This allows for responses that take into account the user's past emotional states. Furthermore, the emotion history unit can analyze the user's emotional change patterns and predict future emotions. This allows for the preparation of appropriate responses in advance based on predicted emotions. Additionally, the emotion history unit can visualize the user's emotional history, helping the user understand their own emotional changes. This allows the user to grasp their emotional state and take appropriate action. Some or all of the above processing in the emotion history unit may be performed using AI, for example, or not. For example, the emotion history unit can input the user's emotion history data into AI and have the AI ​​perform the emotional analysis.

[0097] The following briefly describes the processing flow for example form 2.

[0098] Step 1: The reception desk receives information from users. This information includes work procedures, manuals, and personal experience. For example, users can input work procedures, manuals, and past project experience. Step 2: The generation unit uses a generation AI to analyze the information received by the reception unit and generate a chatbot. The generation AI, for example, uses a text generation AI (e.g., LLM) to analyze the user's input information and generate the optimal chatbot. The generation unit can also generate a chatbot that provides support for business operations based on business procedure manuals. Step 3: The deployment unit deploys the chatbot generated by the generation unit within the organization. The deployment unit can deploy the generated chatbot so that other employees can use it, and it can be deployed to specific departments or the entire organization.

[0099] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0100] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0101] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0102] Each of the multiple elements, including the reception unit, generation unit, and deployment unit described above, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the reception device 38 of the smart device 14 and receives information from the user. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates a chatbot using a generation AI. The deployment unit is implemented by the specific processing unit 290 of the data processing unit 12 and deploys the generated chatbot within the organization. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0103] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0104] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0105] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0106] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0107] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0108] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0109] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0110] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0111] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0112] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0113] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0114] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0115] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0116] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0117] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0118] Each of the multiple elements, including the reception unit, generation unit, and deployment unit described above, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the smart glasses 214 and receives information from the user. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and generates a chatbot using a generation AI. The deployment unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and deploys the generated chatbot within the organization. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0119] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0120] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0121] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0122] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0123] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0124] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0125] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0126] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0127] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0128] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0129] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0130] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0131] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0132] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0133] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0134] Each of the multiple elements described above, including the reception unit, generation unit, and deployment unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the headset terminal 314 and receives information from the user. The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and generates a chatbot using a generation AI. The deployment unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and deploys the generated chatbot within the organization. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0135] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0136] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0137] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0138] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0139] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0140] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0141] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0142] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0143] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0144] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0145] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0146] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0147] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0148] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0149] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0150] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0151] Each of the multiple elements, including the reception unit, generation unit, and deployment unit described above, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the robot 414 and receives information from the user. The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and generates a chatbot using a generation AI. The deployment unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and deploys the generated chatbot within the organization. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0152] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0153] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0154] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0155] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0156] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0157] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0158] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0159] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0160] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0161] 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.

[0162] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0163] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0164] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0165] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0166] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0167] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0168] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0169] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0170] (Note 1) A reception desk that receives information from users, A generation unit analyzes the information received by the reception unit and generates a chatbot, The system comprises a deployment unit that deploys the chatbot generated by the generation unit within the organization. A system characterized by the following features. (Note 2) The aforementioned reception unit is Users input information such as work procedures, manuals, and personal experiences. The system described in Appendix 1, characterized by the features described herein. (Note 3) The generating unit is The AI ​​generates a chatbot that analyzes the information entered by the user and creates the optimal chatbot. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned unfolding section is Deploy the generated chatbot so that other employees within the organization can use it. The system described in Appendix 1, characterized by the features described herein. (Note 5) The generating unit is Using AI generation, a chatbot provides support for tasks based on work procedure manuals. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned unfolding section is By allowing other employees to use it, we aim to improve work efficiency. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is It estimates the user's emotions and adjusts the timing of information reception based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is Analyze the user's past information input history to select the optimal reception method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When receiving information, filtering is performed based on the user's current projects and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is It estimates the user's emotions and determines the priority of the information to be received based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When receiving information, the system prioritizes receiving highly relevant information by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When receiving information, the system analyzes the user's social media activity and collects relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 13) The generating unit is It estimates the user's emotions and adjusts how the chatbot is generated based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The generating unit is When generating a chatbot, adjust the level of detail based on the importance of the input information. The system described in Appendix 1, characterized by the features described herein. (Note 15) The generating unit is When generating a chatbot, different generation algorithms are applied depending on the category of information. The system described in Appendix 1, characterized by the features described herein. (Note 16) The generating unit is We estimate the user's emotions and adjust the chatbot's functionality based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The generating unit is When generating chatbots, the generation priority is determined based on when the information was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 18) The generating unit is When generating chatbots, the generation order is adjusted based on the relevance of the information. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned unfolding section is It estimates the user's emotions and adjusts the chatbot's deployment based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned unfolding section is When deploying a chatbot, the optimal deployment method is selected by referring to past deployment data. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned unfolding section is When deploying chatbots, customize the deployment method according to specific departments or projects within the organization. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned unfolding section is It estimates the user's emotions and adjusts the deployment order of the chatbot based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned unfolding section is When deploying chatbots, select the optimal deployment method considering the geographical distribution within the organization. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned unfolding section is When deploying chatbots, refer to relevant projects within the organization to improve the accuracy of the deployment. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0171] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception desk that receives information from users, A generation unit analyzes the information received by the reception unit and generates a chatbot, The system comprises a deployment unit that deploys the chatbot generated by the generation unit within the organization. A system characterized by the following features.

2. The aforementioned reception unit is Users input information such as work procedures, manuals, and personal experiences. The system according to feature 1.

3. The generating unit is The AI ​​generates a chatbot that analyzes the information entered by the user and creates the optimal chatbot. The system according to feature 1.

4. The aforementioned unfolding section is Deploy the generated chatbot so that other employees within the organization can use it. The system according to feature 1.

5. The generating unit is AI-generated text allows a chatbot to provide support for tasks based on operational procedures. The system according to feature 1.

6. The aforementioned unfolding section is By allowing other employees to use it, we aim to improve work efficiency. The system according to feature 1.

7. The aforementioned reception unit is It estimates the user's emotions and adjusts the timing of information reception based on the estimated user emotions. The system according to feature 1.

8. The aforementioned reception unit is Analyze the user's past information input history to select the optimal reception method. The system according to feature 1.

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