system

Abstract

The system according to this embodiment aims to replicate the personality of the CEO and enable users to receive insights and ideas in a timely manner. [Solution] The system according to this embodiment comprises a reception unit, a generation unit, and a provision unit. The reception unit receives questions from users. The generation unit analyzes the questions received by the reception unit and generates answers by recreating the personality of the CEO. The provision unit provides the answers generated by the generation unit to the user.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, it is difficult to timely receive the insights and ideas of the CEO, which may affect the business quality and the speed of decision-making.

[0005] The system according to the embodiment aims to reproduce the personality of the CEO and enable the user to timely receive insights and ideas.

Means for Solving the Problems

[0006] The system according to the embodiment includes a reception unit, a generation unit, and a provision unit. The reception unit receives questions from the user. The generation unit analyzes the questions received by the reception unit, reproduces the personality of the CEO, and generates an answer. The provision unit provides the answer generated by the generation unit to the user. [Effects of the Invention]

[0007] The system according to this embodiment replicates the personality of the CEO, allowing users to receive insights and ideas in a timely manner. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The advisory system according to an embodiment of the present invention is a system that creates a CEO's personality, reproduced by AI, on a generating AI and utilizes it as an advisor for daily operations and business activities. The advisory system allows users to input arbitrary questions in a chat format. Next, the generating AI analyzes the question, reproduces the CEO's personality, and generates an answer. The generated answer is then provided to the user. This system allows users to receive the CEO's insights and ideas in a timely manner, leading to improved work quality, increased efficiency, and faster decision-making. For example, if a user asks, "I would like advice on how to proceed with a new project," the generating AI provides specific advice based on the CEO's expertise. The system is equipped with a mechanism for linking vast amounts of information, and through construction steps and storage methods to more precisely reproduce the CEO's personality, users can leverage the CEO's extensive experience and knowledge to improve the quality of their work. As a result, the advisory system can provide quick and appropriate answers to user questions.

[0029] The advisory system according to this embodiment comprises a reception unit, a generation unit, and a provision unit. The reception unit receives questions from users. The reception unit can, for example, receive questions from users in a chat format. The reception unit can also convert the questions entered by the user into a format that is easy to analyze. The generation unit uses a generation AI to analyze the questions received by the reception unit and generates answers that reproduce the CEO's personality. The generation unit uses, for example, natural language processing technology to analyze the questions and generates answers based on the CEO's past statements and behavioral patterns. The generation unit can use a generation AI (e.g., text generation AI or multimodal generation AI) to generate appropriate answers to questions. The generation unit can also accumulate the CEO's knowledge and ideas and utilize them in generating answers. The provision unit provides the answers generated by the generation unit to the user. The provision unit can provide answers by methods such as a chatbot, email, or notification. The provision unit provides answers in an appropriate manner so that the user can obtain answers quickly. As a result, the advisory system according to this embodiment can provide quick and appropriate answers to user questions. Some or all of the above-described processes in the generation unit may be performed using a generation AI, or they may not be performed using a generation AI. For example, the generation unit can input a user's question into a generation AI, which can then reproduce the CEO's personality and generate an answer. Some or all of the above-described processes in the delivery unit may be performed using an AI, or they may not be performed using an AI. For example, the delivery unit can use an AI to provide the generated answer to the user.

[0030] The reception desk receives questions from users. For example, it can receive questions via chat. Specifically, users open a chat window through a website or mobile application and enter their questions. The reception desk can also convert user-entered questions into a format that is easy to analyze. For example, it can use natural language processing technology to syntactically analyze the user's question and extract keywords and important phrases. This clarifies the intent and content of the question, allowing for smoother subsequent processing. The reception desk can also categorize user questions and route them to the appropriate processing path. For example, categorizing questions into different categories such as technical questions, business strategy questions, and questions seeking personal advice makes it easier for the generation department to produce more appropriate answers. Furthermore, the reception desk can refer to the user's past question history and profile information to supplement the background information of the question. This enables personalized responses tailored to the user's needs and interests. The reception desk is designed with user interface usability in mind, ensuring intuitive operation. For example, it provides an auto-completion function during question entry and displays a list of frequently asked questions to help users enter questions smoothly. This allows the reception department to efficiently and accurately receive user inquiries and quickly pass them on to the next processing step.

[0031] The generation unit uses generative AI to analyze questions received by the reception unit and generate answers that reflect the CEO's personality. For example, the generation unit uses natural language processing technology to analyze questions and generates answers based on the CEO's past statements and behavioral patterns. Specifically, the generation AI performs contextual and semantic analysis to understand the content of the questions. This helps to grasp the intent and background of the questions, laying the foundation for generating appropriate answers. Next, the generation AI refers to a database of the CEO's past statements and extracts past answers and behavioral patterns to similar questions. This allows the generation of answers that reflect the CEO's thought process and values. The generation unit can generate appropriate answers to questions using generative AI (e.g., text generation AI or multimodal generation AI). Text generation AI generates answers in natural language, while multimodal generation AI can generate answers that include not only text but also other media formats such as images and audio. The generation unit can also accumulate the CEO's insights and ideas and utilize them in answer generation. For example, it can store the content of past speeches and interviews given by the CEO in a database and generate answers based on this information. Furthermore, the generation unit can evaluate the quality of the generated answers and make corrections or improvements as needed. For example, if the generated answer is inappropriate or does not adequately address the user's question, the generation unit will re-analyze it and generate a more appropriate answer. This allows the generation unit to provide high-quality and reliable answers to user questions.

[0032] The service provider delivers the answers generated by the generation unit to the user. The service provider can deliver answers through methods such as chatbots, email, and notifications. Specifically, a chatbot displays the answer directly in the chat window where the user enters their question, allowing the user to receive an answer in real time. In the case of email, the generated answer can be sent to the user's email address, providing a more detailed answer and related information. In the case of notifications, the user is notified that an answer has been generated using the notification function of their smartphone or desktop. The service provider delivers answers in an appropriate manner to ensure that users receive answers quickly. For example, for urgent questions, an answer is provided immediately via chatbot, and if a more detailed explanation is needed, supplementary information is sent via email. The service provider can also customize the method of answer delivery according to the user's preferences. For example, if the user prefers a chat format, the service provider will prioritize the chatbot, and if the user prefers an email response, email will be selected. Furthermore, the service provider can collect user feedback to improve the quality and delivery method of answers. For example, users can provide ratings and comments on answers, allowing the service provider to use that information to improve its service. This enables the service provider to provide users with quick and appropriate answers, thereby improving user satisfaction.

[0033] The generation unit can recreate the CEO's personality using a generative AI. For example, the generation unit can recreate the CEO's personality based on past statements and behavioral patterns using the generative AI. The generation unit can also generate answers based on the CEO's insights and ideas using the generative AI. The generation unit can also build a model to recreate the CEO's personality using the generative AI and generate answers using that model. This allows for more precise answers by recreating the CEO's personality using the generative AI. Some or all of the above-described processes in the generation unit may be performed using the generative AI or not. For example, the generation unit can input data to recreate the CEO's personality into the generative AI, and the generative AI can recreate the personality based on that data.

[0034] The generation unit can analyze questions and generate answers using a generation AI. For example, the generation unit can analyze questions using natural language processing technology and generate appropriate answers. The generation unit can also use a generation AI to analyze the content of questions and generate answers based on the CEO's insights and ideas. The generation unit can also use a generation AI to understand the intent of a question and generate answers based on that intent. In this way, by analyzing questions and generating answers using a generation AI, appropriate answers can be provided to users' questions. Some or all of the above-described processes in the generation unit may be performed using a generation AI or not. For example, the generation unit can input a user's question into a generation AI, which can then analyze the question and generate an answer.

[0035] The service provider can provide the generated answers to the user. The service provider can provide answers using, for example, a chatbot. The service provider can also provide answers via email. The service provider can also provide answers via notifications. This allows users to quickly obtain answers by providing them with generated answers. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can provide the generated answers to the user using AI.

[0036] The reception desk can receive questions from users in a chat format. For example, the reception desk can receive questions from users using a chat interface. The reception desk can also convert the questions entered by users into a format that is easy to analyze. The reception desk can also receive questions entered by users in real time. This makes it easy for users to enter questions by accepting them in a chat format. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can convert user questions into a format that is easy to analyze using AI.

[0037] The generation unit can accumulate the CEO's knowledge and ideas and utilize them to generate answers. For example, the generation unit can build a database to accumulate the CEO's knowledge and ideas. Based on the accumulated knowledge and ideas, the generation unit can generate appropriate answers to questions. The generation unit can also classify the CEO's knowledge and ideas and utilize them to generate answers. This allows for answers based on richer information by accumulating the CEO's knowledge and ideas. Some or all of the above-described processes in the generation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the generation unit can input the CEO's knowledge and ideas into a generation AI, and the generation AI can generate answers based on that data.

[0038] The reception desk can analyze a user's past question history and select the most suitable reception method. For example, the reception desk can prioritize topics that the user has frequently asked about in the past. The reception desk can also prioritize suggesting question formats (text, voice, etc.) that the user has used in the past. The reception desk can also suggest the most suitable reception method for a specific time of day based on the user's past question history. In this way, by analyzing past question 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 or not. For example, the reception desk can input the user's past question history into an AI, and the AI ​​can select the most suitable reception method based on that data.

[0039] The reception desk can filter questions based on the user's current work situation and areas of interest when receiving them. For example, the reception desk can prioritize questions related to projects the user is currently working on. The reception desk can also filter questions based on the user's areas of interest to ensure they are relevant. The reception desk can also suggest appropriate questions based on the user's work situation. This allows for priority reception of relevant questions by filtering them based on the user's work situation and areas of interest. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input user work situation data into an AI, which can then filter questions based on that data.

[0040] The reception desk can prioritize receiving questions that are highly relevant, taking into account the user's geographical location. For example, if the user is in a specific region, the reception desk will prioritize questions related to that region. If the user is on a business trip, the reception desk can also prioritize questions related to their destination. If the user is at home, the reception desk can also prioritize questions related to their home. In this way, by considering the user's geographical location, the reception desk can prioritize receiving questions that are highly relevant. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's geographical location into the AI, and the AI ​​can filter questions based on that data.

[0041] The reception desk can analyze the user's social media activity when receiving questions and accept relevant questions. For example, the reception desk can prioritize questions related to topics the user is discussing on social media. The reception desk can also analyze the content of the user's social media posts and suggest relevant questions. The reception desk can also determine question priorities by considering the user's number of followers and influence on social media. This allows the reception desk to prioritize highly relevant questions by analyzing the user's social media activity. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's social media data into an AI, which can then filter questions based on that data.

[0042] The generation unit can adjust the level of detail in the answer based on the importance of the question when generating the answer. For example, the generation unit can generate a detailed answer for important questions. The generation unit can also generate a concise answer for general questions. The generation unit can also generate an answer quickly for urgent questions. This allows for the provision of an appropriate level of answer by adjusting the level of detail in the answer based on the importance of the question. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input question importance data into the generation AI, and the generation AI can adjust the level of detail in the answer based on that data.

[0043] The generation unit can apply different generation algorithms depending on the question category when generating answers. For example, the generation unit can apply a specialized generation algorithm to technical questions. For questions related to management, the generation unit can also apply a generation algorithm based on management knowledge. For questions related to human resources, the generation unit can also apply a generation algorithm based on human resources knowledge. By applying different generation algorithms depending on the question category, more appropriate answers can be provided. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input question category data into a generation AI, and the generation AI can apply an appropriate generation algorithm based on that data.

[0044] The generation unit can determine the priority of answers based on when the questions were submitted when generating answers. For example, the generation unit will generate answers with the highest priority for urgent questions. The generation unit can also generate answers with normal priority for regular questions. The generation unit can also postpone generating answers for past questions. This allows answers to be provided in the appropriate order by determining the priority of answers based on when the questions were submitted. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input question submission date data into a generation AI, and the generation AI can determine the priority of answers based on that data.

[0045] The generation unit can adjust the order of answers based on the relevance of the questions when generating answers. For example, the generation unit can prioritize generating answers for highly relevant questions. The generation unit can also postpone generating answers for less relevant questions. The generation unit can also adjust the order of answers based on the relevance of the questions. This allows for the provision of more relevant answers by adjusting the order of answers based on the relevance of the questions. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input question relevance data into a generation AI, and the generation AI can adjust the order of answers based on that data.

[0046] The delivery unit can select the optimal delivery method by referring to the user's past feedback when providing responses. For example, the delivery unit may prioritize using delivery methods that the user has preferred in the past. The delivery unit can also select the optimal delivery method from the user's past feedback. The delivery unit can also analyze the user's past feedback and improve the delivery method. This allows the delivery unit to select the optimal delivery method by referring to the user's past feedback. Some or all of the above processes in the delivery unit may be performed using AI or not. For example, the delivery unit can input the user's past feedback data into AI, and the AI ​​can select the optimal delivery method based on that data.

[0047] The response system can adjust the timing of response delivery based on the user's current work situation. For example, if the user is busy, the system may delay providing the response. If the user is relaxed, the system may provide the response immediately. The system can also provide the response at an appropriate time depending on the user's work situation. By adjusting the timing of response delivery according to the user's work situation, the system can provide the response at an appropriate time. Some or all of the above processes in the response system may be performed using AI or not. For example, the response system can input user work situation data into the AI, and the AI ​​can adjust the timing of response delivery based on that data.

[0048] The information delivery unit can select the optimal delivery method when providing responses, taking into account the user's geographical location. For example, if the user is in a specific region, the unit can prioritize providing information related to that region. If the user is on a business trip, the unit can also prioritize providing information related to the destination. If the user is at home, the unit can also prioritize providing information related to home. This allows the unit to select the optimal delivery method by considering the user's geographical location. Some or all of the above processing in the information delivery unit may be performed using AI or not. For example, the information delivery unit can input the user's geographical location into the AI, and the AI ​​can select the optimal delivery method based on that data.

[0049] The information delivery unit can analyze the user's social media activity and propose a method of delivery when providing responses. For example, the information delivery unit can prioritize providing information related to topics the user is discussing on social media. The information delivery unit can also analyze the content of the user's social media posts and propose relevant information. The information delivery unit can also determine how to deliver information by considering the user's number of social media followers and influence. In this way, by analyzing the user's social media activity, the optimal method of delivery can be proposed. Some or all of the above processing in the information delivery unit may be performed using AI or not. For example, the information delivery unit can input the user's social media data into AI, and the AI ​​can propose the optimal method of delivery based on that data.

[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 advisory system may also include a feedback collection unit. This unit allows users to input feedback on the provided answers. For example, it may provide an interface for users to evaluate the usefulness and satisfaction level of the answers. The feedback collection unit can also analyze user feedback and provide feedback data to the generation unit. This allows the generation unit to improve the quality of answers based on the feedback data. The feedback collection unit can also accumulate user feedback and use it for long-term improvement. This can increase user satisfaction and improve the reliability of the system.

[0052] The advisory system can also be equipped with a learning unit. The learning unit can continuously train the generative AI model based on the answers generated by the generation unit and user feedback. For example, the learning unit can analyze patterns of answers that users have given high ratings to and reflect them in the generative AI model. The learning unit can also identify the reasons for answers that users have given low ratings to and improve the generative AI model. The learning unit can also accumulate user feedback and use it as long-term learning data. This allows for continuous improvement of the generative AI model and the provision of higher quality answers.

[0053] The advisory system can also include a data integration unit. This unit can collect information from external data sources and provide it to the generation unit. For example, the data integration unit can collect the latest industry news and market trends, which the generation unit can then use to generate responses. The data integration unit can also link with databases related to the user's business, providing information to the generation unit to generate more specific responses. The data integration unit integrates multiple data sources, enabling the generation unit to generate responses based on comprehensive information. This allows for the provision of more information-rich responses by leveraging external data.

[0054] The advisory system may further include a user authentication unit. The user authentication unit authenticates users when they access the system. For example, the user authentication unit verifies the user's ID and password. The user authentication unit can also implement two-factor authentication to enhance security. The user authentication unit can also perform authentication using the user's biometric authentication (fingerprint, facial recognition, etc.). This enhances user authentication and improves the system's security. Some or all of the above-described processes in the user authentication unit may be performed using AI or not.

[0055] The advisory system may also include a notification unit. The notification unit notifies the user of important information and updates. For example, the notification unit notifies relevant information based on keywords set by the user. The notification unit can also provide notifications at appropriate times based on the user's schedule. The notification unit can also provide customized notifications based on the user's areas of interest. This allows important information to be provided to the user in a timely manner. Some or all of the above processing in the notification unit may be performed using AI or not.

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

[0057] Step 1: The reception desk receives questions from users. For example, it can receive questions from users in a chat format and convert the questions entered by users into a format that is easy to analyze. Step 2: The generation unit uses generational AI to analyze the questions received by the reception unit and generate answers that reflect the CEO's personality. For example, it can analyze questions using natural language processing technology and generate answers based on the CEO's past statements and behavioral patterns. The generation unit can generate appropriate answers to questions using generational AI (e.g., text generation AI or multimodal generation AI). It can also accumulate the CEO's insights and ideas and utilize them in generating answers. Step 3: The providing unit provides the user with the answer generated by the generating unit. For example, the answer can be provided via a chatbot, email, notification, etc. The providing unit provides the answer in an appropriate manner so that the user can get the answer quickly.

[0058] (Example of form 2) The advisory system according to an embodiment of the present invention is a system that creates a CEO's personality, reproduced by AI, on a generating AI and utilizes it as an advisor for daily operations and business activities. The advisory system allows users to input arbitrary questions in a chat format. Next, the generating AI analyzes the question, reproduces the CEO's personality, and generates an answer. The generated answer is then provided to the user. This system allows users to receive the CEO's insights and ideas in a timely manner, leading to improved work quality, increased efficiency, and faster decision-making. For example, if a user asks, "I would like advice on how to proceed with a new project," the generating AI provides specific advice based on the CEO's expertise. The system is equipped with a mechanism for linking vast amounts of information, and through construction steps and storage methods to more precisely reproduce the CEO's personality, users can leverage the CEO's extensive experience and knowledge to improve the quality of their work. As a result, the advisory system can provide quick and appropriate answers to user questions.

[0059] The advisory system according to this embodiment comprises a reception unit, a generation unit, and a provision unit. The reception unit receives questions from users. The reception unit can, for example, receive questions from users in a chat format. The reception unit can also convert the questions entered by the user into a format that is easy to analyze. The generation unit uses a generation AI to analyze the questions received by the reception unit and generates answers that reproduce the CEO's personality. The generation unit uses, for example, natural language processing technology to analyze the questions and generates answers based on the CEO's past statements and behavioral patterns. The generation unit can use a generation AI (e.g., text generation AI or multimodal generation AI) to generate appropriate answers to questions. The generation unit can also accumulate the CEO's knowledge and ideas and utilize them in generating answers. The provision unit provides the answers generated by the generation unit to the user. The provision unit can provide answers by methods such as a chatbot, email, or notification. The provision unit provides answers in an appropriate manner so that the user can obtain answers quickly. As a result, the advisory system according to this embodiment can provide quick and appropriate answers to user questions. Some or all of the above-described processes in the generation unit may be performed using a generation AI, or they may not be performed using a generation AI. For example, the generation unit can input a user's question into a generation AI, which can then reproduce the CEO's personality and generate an answer. Some or all of the above-described processes in the delivery unit may be performed using an AI, or they may not be performed using an AI. For example, the delivery unit can use an AI to provide the generated answer to the user.

[0060] The reception desk receives questions from users. For example, it can receive questions via chat. Specifically, users open a chat window through a website or mobile application and enter their questions. The reception desk can also convert user-entered questions into a format that is easy to analyze. For example, it can use natural language processing technology to syntactically analyze the user's question and extract keywords and important phrases. This clarifies the intent and content of the question, allowing for smoother subsequent processing. The reception desk can also categorize user questions and route them to the appropriate processing path. For example, categorizing questions into different categories such as technical questions, business strategy questions, and questions seeking personal advice makes it easier for the generation department to produce more appropriate answers. Furthermore, the reception desk can refer to the user's past question history and profile information to supplement the background information of the question. This enables personalized responses tailored to the user's needs and interests. The reception desk is designed with user interface usability in mind, ensuring intuitive operation. For example, it provides an auto-completion function during question entry and displays a list of frequently asked questions to help users enter questions smoothly. This allows the reception department to efficiently and accurately receive user inquiries and quickly pass them on to the next processing step.

[0061] The generation unit uses generative AI to analyze questions received by the reception unit and generate answers that reflect the CEO's personality. For example, the generation unit uses natural language processing technology to analyze questions and generates answers based on the CEO's past statements and behavioral patterns. Specifically, the generation AI performs contextual and semantic analysis to understand the content of the questions. This helps to grasp the intent and background of the questions, laying the foundation for generating appropriate answers. Next, the generation AI refers to a database of the CEO's past statements and extracts past answers and behavioral patterns to similar questions. This allows the generation of answers that reflect the CEO's thought process and values. The generation unit can generate appropriate answers to questions using generative AI (e.g., text generation AI or multimodal generation AI). Text generation AI generates answers in natural language, while multimodal generation AI can generate answers that include not only text but also other media formats such as images and audio. The generation unit can also accumulate the CEO's insights and ideas and utilize them in answer generation. For example, it can store the content of past speeches and interviews given by the CEO in a database and generate answers based on this information. Furthermore, the generation unit can evaluate the quality of the generated answers and make corrections or improvements as needed. For example, if the generated answer is inappropriate or does not adequately address the user's question, the generation unit will re-analyze it and generate a more appropriate answer. This allows the generation unit to provide high-quality and reliable answers to user questions.

[0062] The service provider delivers the answers generated by the generation unit to the user. The service provider can deliver answers through methods such as chatbots, email, and notifications. Specifically, a chatbot displays the answer directly in the chat window where the user enters their question, allowing the user to receive an answer in real time. In the case of email, the generated answer can be sent to the user's email address, providing a more detailed answer and related information. In the case of notifications, the user is notified that an answer has been generated using the notification function of their smartphone or desktop. The service provider delivers answers in an appropriate manner to ensure that users receive answers quickly. For example, for urgent questions, an answer is provided immediately via chatbot, and if a more detailed explanation is needed, supplementary information is sent via email. The service provider can also customize the method of answer delivery according to the user's preferences. For example, if the user prefers a chat format, the service provider will prioritize the chatbot, and if the user prefers an email response, email will be selected. Furthermore, the service provider can collect user feedback to improve the quality and delivery method of answers. For example, users can provide ratings and comments on answers, allowing the service provider to use that information to improve its service. This enables the service provider to provide users with quick and appropriate answers, thereby improving user satisfaction.

[0063] The generation unit can recreate the CEO's personality using a generative AI. For example, the generation unit can recreate the CEO's personality based on past statements and behavioral patterns using the generative AI. The generation unit can also generate answers based on the CEO's insights and ideas using the generative AI. The generation unit can also build a model to recreate the CEO's personality using the generative AI and generate answers using that model. This allows for more precise answers by recreating the CEO's personality using the generative AI. Some or all of the above-described processes in the generation unit may be performed using the generative AI or not. For example, the generation unit can input data to recreate the CEO's personality into the generative AI, and the generative AI can recreate the personality based on that data.

[0064] The generation unit can analyze questions and generate answers using a generation AI. For example, the generation unit can analyze questions using natural language processing technology and generate appropriate answers. The generation unit can also use a generation AI to analyze the content of questions and generate answers based on the CEO's insights and ideas. The generation unit can also use a generation AI to understand the intent of a question and generate answers based on that intent. In this way, by analyzing questions and generating answers using a generation AI, appropriate answers can be provided to users' questions. Some or all of the above-described processes in the generation unit may be performed using a generation AI or not. For example, the generation unit can input a user's question into a generation AI, which can then analyze the question and generate an answer.

[0065] The service provider can provide the generated answers to the user. The service provider can provide answers using, for example, a chatbot. The service provider can also provide answers via email. The service provider can also provide answers via notifications. This allows users to quickly obtain answers by providing them with generated answers. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can provide the generated answers to the user using AI.

[0066] The reception desk can receive questions from users in a chat format. For example, the reception desk can receive questions from users using a chat interface. The reception desk can also convert the questions entered by users into a format that is easy to analyze. The reception desk can also receive questions entered by users in real time. This makes it easy for users to enter questions by accepting them in a chat format. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can convert user questions into a format that is easy to analyze using AI.

[0067] The generation unit can accumulate the CEO's knowledge and ideas and utilize them to generate answers. For example, the generation unit can build a database to accumulate the CEO's knowledge and ideas. Based on the accumulated knowledge and ideas, the generation unit can generate appropriate answers to questions. The generation unit can also classify the CEO's knowledge and ideas and utilize them to generate answers. This allows for answers based on richer information by accumulating the CEO's knowledge and ideas. Some or all of the above-described processes in the generation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the generation unit can input the CEO's knowledge and ideas into a generation AI, and the generation AI can generate answers based on that data.

[0068] The reception unit can estimate the user's emotions and adjust the timing of question reception based on the estimated emotions. For example, the reception unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The reception unit can also record the user's voice and estimate their emotions using voice analysis technology. The reception unit can also analyze the user's text input and estimate their emotions. This allows the reception unit to adjust the timing of question reception according to the user's emotions, enabling questions to be received at a more appropriate time. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception unit may be performed using AI or not. For example, the reception unit can input user emotion data into an AI, and the AI ​​can estimate emotions based on that data.

[0069] The reception desk can analyze a user's past question history and select the most suitable reception method. For example, the reception desk can prioritize topics that the user has frequently asked about in the past. The reception desk can also prioritize suggesting question formats (text, voice, etc.) that the user has used in the past. The reception desk can also suggest the most suitable reception method for a specific time of day based on the user's past question history. In this way, by analyzing past question 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 or not. For example, the reception desk can input the user's past question history into an AI, and the AI ​​can select the most suitable reception method based on that data.

[0070] The reception desk can filter questions based on the user's current work situation and areas of interest when receiving them. For example, the reception desk can prioritize questions related to projects the user is currently working on. The reception desk can also filter questions based on the user's areas of interest to ensure they are relevant. The reception desk can also suggest appropriate questions based on the user's work situation. This allows for priority reception of relevant questions by filtering them based on the user's work situation and areas of interest. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input user work situation data into an AI, which can then filter questions based on that data.

[0071] The reception desk can estimate the user's emotions and determine the priority of questions to be received based on the estimated emotions. For example, the reception desk can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The reception desk can also record the user's voice and estimate their emotions using voice analysis technology. The reception desk can also analyze the user's text input and estimate their emotions. This allows questions to be received in a more appropriate order by determining the priority of questions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input user emotion data into an AI, and the AI ​​can determine the priority of questions based on that data.

[0072] The reception desk can prioritize receiving questions that are highly relevant, taking into account the user's geographical location. For example, if the user is in a specific region, the reception desk will prioritize questions related to that region. If the user is on a business trip, the reception desk can also prioritize questions related to their destination. If the user is at home, the reception desk can also prioritize questions related to their home. In this way, by considering the user's geographical location, the reception desk can prioritize receiving questions that are highly relevant. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's geographical location into the AI, and the AI ​​can filter questions based on that data.

[0073] The reception desk can analyze the user's social media activity when receiving questions and accept relevant questions. For example, the reception desk can prioritize questions related to topics the user is discussing on social media. The reception desk can also analyze the content of the user's social media posts and suggest relevant questions. The reception desk can also determine question priorities by considering the user's number of followers and influence on social media. This allows the reception desk to prioritize highly relevant questions by analyzing the user's social media activity. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's social media data into an AI, which can then filter questions based on that data.

[0074] The generation unit can estimate the user's emotions and adjust the way the response is expressed based on the estimated emotions. For example, the generation unit can capture the user's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. The generation unit can also record the user's voice and estimate the emotions using voice analysis technology. The generation unit can also analyze the user's text input and estimate the emotions. This allows for the provision of more appropriate responses by adjusting the way the response is expressed according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation 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 generation AI or not. For example, the generation unit can input user emotion data into a generation AI, and the generation AI can adjust the way the response is expressed based on that data.

[0075] The generation unit can adjust the level of detail in the answer based on the importance of the question when generating the answer. For example, the generation unit can generate a detailed answer for important questions. The generation unit can also generate a concise answer for general questions. The generation unit can also generate an answer quickly for urgent questions. This allows for the provision of an appropriate level of answer by adjusting the level of detail in the answer based on the importance of the question. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input question importance data into the generation AI, and the generation AI can adjust the level of detail in the answer based on that data.

[0076] The generation unit can apply different generation algorithms depending on the question category when generating answers. For example, the generation unit can apply a specialized generation algorithm to technical questions. For questions related to management, the generation unit can also apply a generation algorithm based on management knowledge. For questions related to human resources, the generation unit can also apply a generation algorithm based on human resources knowledge. By applying different generation algorithms depending on the question category, more appropriate answers can be provided. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input question category data into a generation AI, and the generation AI can apply an appropriate generation algorithm based on that data.

[0077] The generation unit can estimate the user's emotions and adjust the length of the response based on the estimated emotions. For example, if the user is in a hurry, the generation unit can generate a short, concise response. If the user is relaxed, the generation unit can also generate a longer response that includes detailed explanations. If the user is excited, the generation unit can also generate a response with visually stimulating effects. This allows for the provision of more appropriate responses by adjusting the length of the response according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation 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 processing in the generation unit may be performed using or without a generation AI. For example, the generation unit can input user emotion data into a generation AI, which can then adjust the length of the response based on that data.

[0078] The generation unit can determine the priority of answers based on when the questions were submitted when generating answers. For example, the generation unit will generate answers with the highest priority for urgent questions. The generation unit can also generate answers with normal priority for regular questions. The generation unit can also postpone generating answers for past questions. This allows answers to be provided in the appropriate order by determining the priority of answers based on when the questions were submitted. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input question submission date data into a generation AI, and the generation AI can determine the priority of answers based on that data.

[0079] The generation unit can adjust the order of answers based on the relevance of the questions when generating answers. For example, the generation unit can prioritize generating answers for highly relevant questions. The generation unit can also postpone generating answers for less relevant questions. The generation unit can also adjust the order of answers based on the relevance of the questions. This allows for the provision of more relevant answers by adjusting the order of answers based on the relevance of the questions. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input question relevance data into a generation AI, and the generation AI can adjust the order of answers based on that data.

[0080] The service provider can estimate the user's emotions and adjust the method of providing responses based on the estimated emotions. For example, the service provider can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The service provider can also record the user's voice and estimate their emotions using voice analysis technology. The service provider can also analyze the user's text input and estimate their emotions. This allows the service provider to provide responses in a more appropriate way by adjusting the method of providing responses according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the service provider may be performed using AI or not. For example, the service provider can input user emotion data into an AI, and the AI ​​can adjust the method of providing responses based on that data.

[0081] The delivery unit can select the optimal delivery method by referring to the user's past feedback when providing responses. For example, the delivery unit may prioritize using delivery methods that the user has preferred in the past. The delivery unit can also select the optimal delivery method from the user's past feedback. The delivery unit can also analyze the user's past feedback and improve the delivery method. This allows the delivery unit to select the optimal delivery method by referring to the user's past feedback. Some or all of the above processes in the delivery unit may be performed using AI or not. For example, the delivery unit can input the user's past feedback data into AI, and the AI ​​can select the optimal delivery method based on that data.

[0082] The response system can adjust the timing of response delivery based on the user's current work situation. For example, if the user is busy, the system may delay providing the response. If the user is relaxed, the system may provide the response immediately. The system can also provide the response at an appropriate time depending on the user's work situation. By adjusting the timing of response delivery according to the user's work situation, the system can provide the response at an appropriate time. Some or all of the above processes in the response system may be performed using AI or not. For example, the response system can input user work situation data into the AI, and the AI ​​can adjust the timing of response delivery based on that data.

[0083] The service provider can estimate the user's emotions and determine the priority of the responses to be provided based on the estimated emotions. For example, the service provider can capture the user's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. The service provider can also record the user's voice and estimate the emotions using voice analysis technology. The service provider can also analyze the user's text input and estimate the emotions. This allows the service provider to provide responses in a more appropriate order by determining the priority of responses according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the service provider may be performed using AI or not. For example, the service provider can input user emotion data into an AI, and the AI ​​can determine the priority of responses based on that data.

[0084] The information delivery unit can select the optimal delivery method when providing responses, taking into account the user's geographical location. For example, if the user is in a specific region, the unit can prioritize providing information related to that region. If the user is on a business trip, the unit can also prioritize providing information related to the destination. If the user is at home, the unit can also prioritize providing information related to home. This allows the unit to select the optimal delivery method by considering the user's geographical location. Some or all of the above processing in the information delivery unit may be performed using AI or not. For example, the information delivery unit can input the user's geographical location into the AI, and the AI ​​can select the optimal delivery method based on that data.

[0085] The information delivery unit can analyze the user's social media activity and propose a method of delivery when providing responses. For example, the information delivery unit can prioritize providing information related to topics the user is discussing on social media. The information delivery unit can also analyze the content of the user's social media posts and propose relevant information. The information delivery unit can also determine how to deliver information by considering the user's number of social media followers and influence. In this way, by analyzing the user's social media activity, the optimal method of delivery can be proposed. Some or all of the above processing in the information delivery unit may be performed using AI or not. For example, the information delivery unit can input the user's social media data into AI, and the AI ​​can propose the optimal method of delivery based on that data.

[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 advisory system may also include a feedback collection unit. This unit allows users to input feedback on the provided answers. For example, it may provide an interface for users to evaluate the usefulness and satisfaction level of the answers. The feedback collection unit can also analyze user feedback and provide feedback data to the generation unit. This allows the generation unit to improve the quality of answers based on the feedback data. The feedback collection unit can also accumulate user feedback and use it for long-term improvement. This can increase user satisfaction and improve the reliability of the system.

[0088] The generation unit can estimate the user's emotions and adjust the tone of the response based on the estimated emotions. For example, if the user is stressed, the generation unit will generate a response in a gentle tone. If the user is excited, the generation unit can also generate a response in a calm tone. If the user is relaxed, the generation unit can also generate a response in a friendly tone. This allows for more appropriate communication by adjusting the tone of the response according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI.

[0089] The advisory system can also be equipped with a learning unit. The learning unit can continuously train the generative AI model based on the answers generated by the generation unit and user feedback. For example, the learning unit can analyze patterns of answers that users have given high ratings to and reflect them in the generative AI model. The learning unit can also identify the reasons for answers that users have given low ratings to and improve the generative AI model. The learning unit can also accumulate user feedback and use it as long-term learning data. This allows for continuous improvement of the generative AI model and the provision of higher quality answers.

[0090] The generation unit can estimate the user's emotions and customize the content of the response based on the estimated emotions. For example, if the user is feeling anxious, the generation unit will generate a response that provides reassurance. If the user is excited, the generation unit can also generate a response that encourages calmness. If the user is relaxed, the generation unit can also generate a response that includes detailed explanations. By customizing the content of the response according to the user's emotions, more appropriate information can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI.

[0091] The advisory system can also include a data integration unit. This unit can collect information from external data sources and provide it to the generation unit. For example, the data integration unit can collect the latest industry news and market trends, which the generation unit can then use to generate responses. The data integration unit can also link with databases related to the user's business, providing information to the generation unit to generate more specific responses. The data integration unit integrates multiple data sources, enabling the generation unit to generate responses based on comprehensive information. This allows for the provision of more information-rich responses by leveraging external data.

[0092] The generation unit can estimate the user's emotions and adjust the visual representation of the response based on the estimated emotions. For example, if the user is stressed, the generation unit will generate a response that includes graphics in calming colors. If the user is excited, the generation unit may also generate a response that includes simple, visually less stimulating graphics. If the user is relaxed, the generation unit may also generate a response that includes detailed graphics or charts. This allows for more appropriate information to be provided by adjusting the visual representation according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. Some or all of the above-described processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI.

[0093] The advisory system may further include a user authentication unit. The user authentication unit authenticates users when they access the system. For example, the user authentication unit verifies the user's ID and password. The user authentication unit can also implement two-factor authentication to enhance security. The user authentication unit can also perform authentication using the user's biometric authentication (fingerprint, facial recognition, etc.). This enhances user authentication and improves the system's security. Some or all of the above-described processes in the user authentication unit may be performed using AI or not.

[0094] The generation unit can estimate the user's emotions and adjust the timing of response delivery based on the estimated emotions. For example, if the user is stressed, the generation unit will provide a response quickly. If the user is relaxed, the generation unit can also provide a detailed response. If the user is excited, the generation unit can provide a response at a calm time. By adjusting the timing of response delivery according to the user's emotions, more appropriate information can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI.

[0095] The advisory system may also include a notification unit. The notification unit notifies the user of important information and updates. For example, the notification unit notifies relevant information based on keywords set by the user. The notification unit can also provide notifications at appropriate times based on the user's schedule. The notification unit can also provide customized notifications based on the user's areas of interest. This allows important information to be provided to the user in a timely manner. Some or all of the above processing in the notification unit may be performed using AI or not.

[0096] The generation unit can estimate the user's emotions and adjust the level of detail in the response based on the estimated emotions. For example, if the user is stressed, the generation unit will generate a concise and to-the-point response. If the user is relaxed, the generation unit can also generate a response that includes detailed explanations. If the user is agitated, the generation unit can also generate a response that includes calming content. By adjusting the level of detail in the response according to the user's emotions, more appropriate information can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI.

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

[0098] Step 1: The reception desk receives questions from users. For example, it can receive questions from users in a chat format and convert the questions entered by users into a format that is easy to analyze. Step 2: The generation unit uses generational AI to analyze the questions received by the reception unit and generate answers that reflect the CEO's personality. For example, it can analyze questions using natural language processing technology and generate answers based on the CEO's past statements and behavioral patterns. The generation unit can generate appropriate answers to questions using generational AI (e.g., text generation AI or multimodal generation AI). It can also accumulate the CEO's insights and ideas and utilize them in generating answers. Step 3: The providing unit provides the user with the answer generated by the generating unit. For example, the answer can be provided via a chatbot, email, notification, etc. The providing unit provides the answer in an appropriate manner so that the user can get the answer quickly.

[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 described above, including the reception unit, generation unit, and provision unit, is implemented, for example, by 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 questions from the user in a chat format. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and uses a generation AI to analyze the questions and generate answers that reproduce the CEO's personality. The provision unit is implemented, for example, by the output device 40 of the smart device 14 and provides the generated answers to the user. The correspondence between each unit and the devices and control units 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 described above, including the reception unit, generation unit, and delivery unit, is implemented 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 questions from the user in voice format. The generation unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the question using a generation AI, recreating the CEO's personality and generating an answer. The delivery unit is implemented by the speaker 240 of the smart glasses 214 and provides the generated answer to the user. 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 provision unit, is implemented in 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 questions from the user in voice format. The generation unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the questions using generation AI, reproducing the CEO's personality and generating answers. The provision unit is implemented by the display 343 of the headset terminal 314 and provides the generated answers to the user. 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 described above, including the reception unit, generation unit, and delivery unit, 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 user questions in voice format. The generation unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and uses a generation AI to analyze the question and generate an answer that replicates the CEO's personality. The delivery unit is implemented by, for example, the speaker 240 of the robot 414 and provides the generated answer to the user. 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.

[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 handles questions from users, A generation unit analyzes the questions received by the reception unit and generates answers by recreating the CEO's personality, The system includes a providing unit that provides the answer generated by the generation unit to the user. A system characterized by the following features. (Note 2) The generating unit is The CEO's personality will be recreated using a generative AI. The system described in Appendix 1, characterized by the features described herein. (Note 3) The generating unit is The AI ​​analyzes the question and generates the answer. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned supply unit is, Provide the generated response to the user. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned reception unit is We accept questions from users in a chat format. The system described in Appendix 1, characterized by the features described herein. (Note 6) The generating unit is Accumulate the CEO's insights and ideas and use them to generate answers. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of question submissions based on those estimated 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 question history and select the most suitable method of handling inquiries. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When receiving questions, filtering is performed based on the user's current work situation and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is The system estimates the user's emotions and prioritizes the questions to be asked based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When receiving questions, the system prioritizes accepting questions that are highly relevant, taking into account 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 a question, the system analyzes the user's social media activity and selects relevant questions. 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 the way responses are expressed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The generating unit is When generating answers, adjust the level of detail in the answers based on the importance of the question. The system described in Appendix 1, characterized by the features described herein. (Note 15) The generating unit is When generating answers, different generation algorithms are applied depending on the question category. The system described in Appendix 1, characterized by the features described herein. (Note 16) The generating unit is It estimates the user's emotions and adjusts the length of the response based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The generating unit is When generating answers, the system prioritizes answers based on when the questions were submitted. The system described in Appendix 1, characterized by the features described herein. (Note 18) The generating unit is When generating answers, the order of answers is adjusted based on the relevance of the questions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned supply unit is, We estimate the user's emotions and adjust how we provide responses based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned supply unit is, When providing responses, the system will refer to the user's past feedback to select the most suitable method of delivery. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned supply unit is, When providing responses, the timing of the response will be adjusted based on the user's current work situation. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned supply unit is, It estimates the user's emotions and determines the priority of the responses to provide based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned supply unit is, When providing responses, the optimal method of delivery will be selected, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned supply unit is, When providing responses, we analyze the user's social media activity and suggest methods for providing the responses. 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 handles questions from users, A generation unit analyzes the questions received by the reception unit and generates answers by recreating the CEO's personality, The system includes a providing unit that provides the answer generated by the generation unit to the user. A system characterized by the following features.

2. The generating unit is Recreating the CEO's personality using generative AI. The system according to feature 1.

3. The generating unit is The AI ​​generates questions and produces answers. The system according to feature 1.

4. The aforementioned supply unit is, Provide the generated response to the user. The system according to feature 1.

5. The aforementioned reception unit is We accept questions from users in a chat format. The system according to feature 1.

6. The generating unit is Accumulate the CEO's insights and ideas and use them to generate answers. The system according to feature 1.

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

8. The aforementioned reception unit is Analyze the user's past question history and select the most suitable method of handling inquiries. The system according to feature 1.

9. The aforementioned reception unit is When receiving questions, filtering is performed based on the user's current work situation and areas of interest. The system according to feature 1.

10. The aforementioned reception unit is The system estimates the user's emotions and prioritizes the questions to be asked based on those estimated emotions. The system according to feature 1.

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